CTNF 19/073,537 CTNF 89353 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. 08-34 AIA Claim s 1 – 19 and 21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1, 4 – 7, 9, 13 – 15, 17, and 19 of U.S. Patent No. 11,388, 397 . Although the claims at issue are not identical, they are not patentably distinct from each other because the scope of the patented claims encompasses the scope of the currently claimed invention . Current Application U.S. Patent No. 11,388,397 1. A picture component prediction method, applied to a decoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set , wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 1. A picture component prediction method, applied to a decoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set , wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values , wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and a corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value . 4. The method of claim 3, wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting two reference samples from the samples in the one or more neighboring lines at the top side of the current block according to a preset position, and selecting two reference samples from the samples in the one or more neighboring columns at the left side of the current block according to a preset position ; determining the multiple first picture component reference values according to the four selected samples . 2. The method of claim of claim 1, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values . 3. The method of claim 2, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 5. The method of claim 1, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 4. The method of claim 2, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2 . 13. The method of claim 1, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2. 5. The method of claim 3, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value. 6. The method of claim 5, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value. 6. The method of claim 5, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component. 7. The method of claim 6, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and a corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component . 7. The method of claim 1, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering. 14. The method of claim 1, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering . 8. The method of claim 1, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 15. The method of claim 1, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 9. The method of claim 6, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 9. The method of claim 7, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 10. The method of claim 1, wherein the first picture component is a luma component, and the to-be-predicted picture component is a first or second chroma component; or the first picture component is the first chroma component, and the to-be-predicted picture component is the luma component or the second chroma component; or the first picture component is the second chroma component, and the to-be-predicted picture component is the luma component or the first chroma component; or the first picture component is a first colour component, and the to-be-predicted picture component is a second colour component or a third colour component; or the first picture component is the second colour component, and the to-be-predicted picture component is the first colour component or the third colour component; or the first picture component is the third colour component, and the to-be-predicted picture component is the second colour component or the first colour component, wherein the first colour component is a red component, the second colour component is a green component, and the third colour component is a blue component. 17. The method of claim 1, wherein the first picture component is a luma component, and the to-be-predicted picture component is a first or second chroma component; or the first picture component is the first chroma component, and the to-be-predicted picture component is the luma component or the second chroma component; or the first picture component is the second chroma component, and the to-be-predicted picture component is the luma component or the first chroma component; or the first picture component is a first colour component, and the to-be-predicted picture component is a second colour component or a third colour component; or the first picture component is the second colour component, and the to-be-predicted picture component is the first colour component or the third colour component; or the first picture component is the third colour component, and the to-be-predicted picture component is the second colour component or the first colour component, wherein the first colour component is a red component, the second colour component is a green component, and the third colour component is a blue component . 11. A picture component prediction method, applied to an encoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 19. A picture component prediction method, applied to an encoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values , wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, and wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and a corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value . 4. The method of claim 3, wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting two reference samples from the samples in the one or more neighboring lines at the top side of the current block according to a preset position, and selecting two reference samples from the samples in the one or more neighboring columns at the left side of the current block according to a preset position; determining the multiple first picture component reference values according to the four selected samples . 12. The method of claim of claim 11, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values. 13. The method of claim 12, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 5. The method of claim 1, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 14. The method of claim 12, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2. 13. The method of claim 1, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2. 15. The method of claim 13, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value . 6. The method of claim 5, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value . 16. The method of claim 15, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component . 7. The method of claim 6, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and a corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component . 17. The method of claim 11, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering. 14. The method of claim 1, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering . 18. The method of claim 11, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 15. The method of claim 1, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 19. The method of claim 16, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 9. The method of claim 7, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product. 21. A non-transitory computer-readable storage medium, having a computer program and a bitstream stored thereon, wherein the computer program, when executed by a processor, enables the processor to perform a picture component prediction method to generate the bitstream, and the method comprises : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 19. A picture component prediction method, applied to an encoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values , wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, and wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and a corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value . 4. The method of claim 3, wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting two reference samples from the samples in the one or more neighboring lines at the top side of the current block according to a preset position, and selecting two reference samples from the samples in the one or more neighboring columns at the left side of the current block according to a preset position; determining the multiple first picture component reference values according to the four selected samples . 08-34 AIA Claim s 1 – 19 and 21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1, 4 – 14, and 16 - 19 of U.S. Patent No. 11,876,958 . Although the claims at issue are not identical, they are not patentably distinct from each other because the scope of the patented claims wholly encompass the scope of the presented invention . Current Application U.S. Patent No. 11,876,958 1. A picture component prediction method, applied to a decoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set , wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 1. A picture component prediction method, applied to a decoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and a corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value . 4. The method of claim 3, wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting two reference samples from the samples in the one or more neighboring lines at the top side of the current block according to a preset position, and selecting two reference samples from the samples in the one or more neighboring columns at the left side of the current block according to a preset position; determining the multiple first picture component reference values according to the four selected samples . 2. The method of claim of claim 1, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values . 5. The method of claim of claim 1, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values . 3. The method of claim 2, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 6. The method of claim 5, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 4. The method of claim 2, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2 . 7. The method of claim 5, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2 . 5. The method of claim 3, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value. 8. The method of claim 6, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value . 6. The method of claim 5, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component. 9. The method of claim 8, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and a corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component. 7. The method of claim 1, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering. 10. The method of claim 1, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering . 8. The method of claim 1, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 11. The method of claim 1, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 9. The method of claim 6, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 12. The method of claim 9, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 10. The method of claim 1, wherein the first picture component is a luma component, and the to-be-predicted picture component is a first or second chroma component; or the first picture component is the first chroma component, and the to-be-predicted picture component is the luma component or the second chroma component; or the first picture component is the second chroma component, and the to-be-predicted picture component is the luma component or the first chroma component; or the first picture component is a first colour component, and the to-be-predicted picture component is a second colour component or a third colour component; or the first picture component is the second colour component, and the to-be-predicted picture component is the first colour component or the third colour component; or the first picture component is the third colour component, and the to-be-predicted picture component is the second colour component or the first colour component, wherein the first colour component is a red component, the second colour component is a green component, and the third colour component is a blue component. 13. The method of claim 1, wherein the first picture component is a luma component, and the to-be-predicted picture component is a first or second chroma component; or the first picture component is the first chroma component, and the to-be-predicted picture component is the luma component or the second chroma component; or the first picture component is the second chroma component, and the to-be-predicted picture component is the luma component or the first chroma component; or the first picture component is a first colour component, and the to-be-predicted picture component is a second colour component or a third colour component; or the first picture component is the second colour component, and the to-be-predicted picture component is the first colour component or the third colour component; or the first picture component is the third colour component, and the to-be-predicted picture component is the second colour component or the first colour component, wherein the first colour component is a red component, the second colour component is a green component, and the third colour component is a blue component . 11. A picture component prediction method, applied to an encoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 14 . A picture component prediction method, applied to an encoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and a corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value . 16. The method of claim 15, wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting two reference samples from the samples in the one or more neighboring lines at the top side of the current block according to a preset position, and selecting two reference samples from the samples in the one or more neighboring columns at the left side of the current block according to a preset position; determining the multiple first picture component reference values according to the four selected samples . 12. The method of claim of claim 11, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values. 17. The method of claim of claim 14, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values . 13. The method of claim 12, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 18. The method of claim 17, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 14. The method of claim 12, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2. 19. The method of claim 17, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2 . 15. The method of claim 13, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value . 8. The method of claim 6, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value . 16. The method of claim 15, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component . 9. The method of claim 8, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and a corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component . 17. The method of claim 11, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering. 10. The method of claim 1, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering . 18. The method of claim 11, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 11. The method of claim 1, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 19. The method of claim 16, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 12. The method of claim 9, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be - predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 21. A non-transitory computer-readable storage medium, having a computer program and a bitstream stored thereon, wherein the computer program, when executed by a processor, enables the processor to perform a picture component prediction method to generate the bitstream, and the method comprises : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 14 . A picture component prediction method, applied to an encoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and a corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value . 16. The method of claim 15, wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting two reference samples from the samples in the one or more neighboring lines at the top side of the current block according to a preset position, and selecting two reference samples from the samples in the one or more neighboring columns at the left side of the current block according to a preset position; determining the multiple first picture component reference values according to the four selected samples . 08-34 AIA Claim s 1 – 19 and 21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1 - 19 of U.S. Patent No. 12,323,584 . Although the claims at issue are not identical, they are not patentably distinct from each other because the scope of the patented case wholly encompasses the scope of the current invention . Current Application U.S. Patent No. 12,323,584 1. A picture component prediction method, applied to a decoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set , wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 1. A picture component prediction method, applied to a decoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting two reference samples from samples in one or more neighboring lines at a top side of the current block according to preset positions, and selecting two reference samples from samples in one or more neighboring columns at a left side of the current block according to preset positions; and determining the multiple first picture component reference values according to the four selected samples . 2. The method of claim of claim 1, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values . 2. The method of claim of claim 1, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values . 3. The method of claim 2, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 3. The method of claim 2, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 4. The method of claim 2, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2 . 4. The method of claim 2, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2 . 5. The method of claim 3, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value. 5. The method of claim 3, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value. 6. The method of claim 5, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component. 6. The method of claim 5, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component . 7. The method of claim 1, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering. 7. The method of claim 1, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering. 8. The method of claim 1, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 8. The method of claim 1, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 9. The method of claim 6, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 9. The method of claim 6, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 10. The method of claim 1, wherein the first picture component is a luma component, and the to-be-predicted picture component is a first or second chroma component; or the first picture component is the first chroma component, and the to-be-predicted picture component is the luma component or the second chroma component; or the first picture component is the second chroma component, and the to-be-predicted picture component is the luma component or the first chroma component; or the first picture component is a first colour component, and the to-be-predicted picture component is a second colour component or a third colour component; or the first picture component is the second colour component, and the to-be-predicted picture component is the first colour component or the third colour component; or the first picture component is the third colour component, and the to-be-predicted picture component is the second colour component or the first colour component, wherein the first colour component is a red component, the second colour component is a green component, and the third colour component is a blue component. 10. The method of claim 1, wherein the first picture component is a luma component, and the to-be-predicted picture component is a first or second chroma component; or the first picture component is the first chroma component, and the to-be-predicted picture component is the luma component or the second chroma component; or the first picture component is the second chroma component, and the to-be-predicted picture component is the luma component or the first chroma component; or the first picture component is a first colour component, and the to-be-predicted picture component is a second colour component or a third colour component; or the first picture component is the second colour component, and the to-be-predicted picture component is the first colour component or the third colour component; or the first picture component is the third colour component, and the to-be-predicted picture component is the second colour component or the first colour component, wherein the first colour component is a red component, the second colour component is a green component, and the third colour component is a blue component . 11. A picture component prediction method, applied to an encoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 11. A picture component prediction method, applied to an encoder, the method comprising: determining a first picture component reference value set of a current block; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting two reference samples from samples in one or more neighboring lines at a top side of the current block according to preset positions, and selecting two reference samples from samples in one or more neighboring columns at a left side of the current block according to preset positions; and determining the multiple first picture component reference values according to the four selected samples. 12. The method of claim of claim 11, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values. 12. The method of claim of claim 11, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values . 13. The method of claim 12, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 13. The method of claim 12, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 14. The method of claim 12, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2. 14. The method of claim 12, wherein a number of sample values in the set of greater first picture component reference values is an integer which is equal to or greater than 1, and a number of sample values in the set of smaller first picture component reference values is an integer which is equal to or greater than 1, wherein the number of sample values in the set of greater first picture component reference values is 2, and a number of sample values in the set of smaller first picture component reference values is 2 . 15. The method of claim 13, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value . 15. The method of claim 13, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value . 16. The method of claim 15, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component . 16. The method of claim 15, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component . 17. The method of claim 11, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering. 17. The method of claim 11, wherein performing mapping processing on the reconstructed value of the first picture component of the current block according to the component linear model to obtain the mapped value comprises: performing second filtering processing on the reconstructed value of the first picture component to obtain a second filtered value of the reconstructed value of the first picture component; and performing mapping processing on the second filtered value according to the component linear model to obtain the mapped value, wherein second filtering processing is down-sampling filtering or low-pass filtering . 18. The method of claim 11, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 18. The method of claim 11, wherein determining the predicted value of the to-be-predicted picture component of the current block according to the mapped value comprises: setting the mapped value as the predicted value of the to-be-predicted picture component of the current block . 19. The method of claim 16, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 19. The method of claim 16, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 21. A non-transitory computer-readable storage medium, having a computer program and a bitstream stored thereon, wherein the computer program, when executed by a processor, enables the processor to perform a picture component prediction method to generate the bitstream, and the method comprises : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 11. A picture component prediction method, applied to an encoder, the method comprising: determining a first picture component reference value set of a current block; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting two reference samples from samples in one or more neighboring lines at a top side of the current block according to preset positions, and selecting two reference samples from samples in one or more neighboring columns at a left side of the current block according to preset positions; and determining the multiple first picture component reference values according to the four selected samples . 08-35 Claim s 1 – 3, 5, 6, 9, 11 – 13, 15, 16, 19, and 21 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1, 4 – 8, 10, 13 – 17 and 19 of copending Application No. 19/354,315 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the scope of the co-pending application encompasses the scope of the current application . This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented. Current Application Co-pending Application No. 19/354,315 1. A picture component prediction method, applied to a decoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set , wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 1. A decoder for picture component prediction , comprising: a memory for storing executable video component prediction instructions; and a processor configured to perform the executable video component prediction instructions stored in the memory to perform following operations: determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set , wherein reference sample values in the filtered reference value set are compared, and a set of greater first picture component reference values and a set of smaller first picture component reference values are determined ; determining multiple filtered first picture reference sample values based on the set of greater first picture component reference values and the set of smaller first picture component reference values ; determining a parameter of a component model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component model characterizing a mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value . 4. The decoder of claim 3, wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting reference samples from the samples in the one or more neighboring lines at the top side of the current block according to two preset positions, and selecting reference samples from the samples in the one or more neighboring columns at the left side of the current block according to two preset positions; determining the multiple first picture component reference values according to the selected samples . 2. The method of claim of claim 1, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values . 3. The method of claim 2, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 5. The decoder of claim 1, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and the set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 5. The method of claim 3, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value. 6. The decoder of claim 5, the operations further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value . 6. The method of claim 5, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component. 7. The decoder of claim 6, wherein determining the parameter of the component model according to the multiple filtered first picture reference sample values and the corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component model characterizing the mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component . 9. The method of claim 6, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product ; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 8. The decoder of claim 7, wherein determining the parameter of the component model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting a multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a product between the minimum first picture component reference value and the multiplicative factor and setting an additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the product. 11. A picture component prediction method, applied to an encoder, the method comprising : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 10dd. An encoder for picture component prediction, comprising: a memory for storing executable video component prediction instructions; and a processor configured to perform the executable video component prediction instructions stored in the memory to perform following operations: determining a first picture component reference value set of a current block; determining multiple first picture component reference values according to the first picture component reference value set; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, wherein reference sample values in the filtered reference value set are compared, and a set of greater first picture component reference values and a set of smaller first picture component reference values are determined; determining multiple filtered first picture reference sample values based on the set of greater first picture component reference values and the set of smaller first picture component reference values; determining a parameter of a component model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component model characterizing a mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component model to obtain a mapped value; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value. 13. The encoder of claim 12, wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting reference samples from the samples in the one or more neighboring lines at the top side of the current block according to two preset positions, and selecting reference samples from the samples in the one or more neighboring columns at the left side of the current block according to two preset positions; determining the multiple first picture component reference values according to the selected samples. 12. The method of claim of claim 11, wherein the first filtering processing is performed on the sample values of the samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set, and wherein reference sample values in the filtered reference value set are compared, and wherein a set of greater first picture component reference values and a set of smaller first picture component reference values are determined based on the reference sample values, wherein the multiple filtered first picture reference sample values are determined by using multiple values from each of the set of greater first picture component reference values and the set of smaller first picture component reference values. 13. The method of claim 12, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value . 14. The encoder of claim 10, wherein determining the multiple filtered first picture reference sample values based on the set of greater first picture component reference values and the set of smaller first picture component reference values comprises: performing mean processing on the set of greater first picture component reference values to obtain a filtered maximum first picture component reference value; and performing mean processing on the set of smaller first picture component reference values to obtain a filtered minimum first picture component reference value. 15. The method of claim 13, further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value . 15. The encoder of claim 14, the operations further comprising: determining a maximum to-be-predicted picture component reference value corresponding to the filtered maximum first picture component reference value and a minimum to-be-predicted picture component reference value corresponding to the filtered minimum first picture component reference value . 16. The method of claim 15, wherein determining the parameter of the component linear model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component linear model characterizing the linear mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component . 16. The encoder of claim 15, wherein determining the parameter of the component model according to the multiple filtered first picture reference sample values and the corresponding to-be-predicted picture component reference values comprises: determining the parameter of the component model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value, the component model characterizing the mapping relationship for mapping the sample value of the first picture component to the sample value of the to-be-predicted picture component. 19. The method of claim 16, wherein determining the parameter of the component linear model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component linear model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting the multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a first product between the maximum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the maximum to-be-predicted picture component reference value and the first product; or calculating a second product between the minimum first picture component reference value and the multiplicative factor and setting the additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the second product . 17. The encoder of claim 16, wherein determining the parameter of the component model according to the filtered maximum first picture component reference value, the maximum to-be-predicted picture component reference value, the filtered minimum first picture component reference value and the minimum to-be-predicted picture component reference value comprises: the parameter of the component model comprises a multiplicative factor and an additive offset; calculating a first difference value between the maximum to-be-predicted picture component reference value and the minimum to-be-predicted picture component reference value; calculating a second difference value between the maximum first picture component reference value and the minimum first picture component reference value; setting a multiplicative factor to be a ratio of the first difference value to the second difference value; and calculating a product between the minimum first picture component reference value and the multiplicative factor and setting an additive offset to be a difference value between the minimum to-be-predicted picture component reference value and the product . 21. A non-transitory computer-readable storage medium, having a computer program and a bitstream stored thereon, wherein the computer program, when executed by a processor, enables the processor to perform a picture component prediction method to generate the bitstream, and the method comprises : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set, wherein the multiple first picture component reference values include four reference values corresponding to four preset sample positions, respectively ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively, to determine multiple filtered first picture reference sample values ; determining a parameter of a component linear model according to the multiple filtered first picture reference sample values and corresponding to-be- predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component linear model characterizing a linear mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model to obtain a mapped value ; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value ; wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: determining the multiple first picture component reference values from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining the multiple first picture component reference values from samples in one or more neighboring columns at a left side of the current block according to four preset positions . 19dd. A non-transitory computer-readable storage medium, having a computer program and a bitstream stored thereon, wherein the computer program, when executed by a processor, enables the processor to perform an encoding method to generate the bitstream, and the encoding method comprises : determining a first picture component reference value set of a current block ; determining multiple first picture component reference values according to the first picture component reference value set ; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values to obtain a filtered reference value set , wherein reference sample values in the filtered reference value set are compared, and a set of greater first picture component reference values and a set of smaller first picture component reference values are determined; determining multiple filtered first picture reference sample values based on the set of greater first picture component reference values and the set of smaller first picture component reference values; determining a parameter of a component model according to the multiple filtered first picture reference sample values and corresponding to-be-predicted picture component reference values, the to-be-predicted picture component being a picture component which is different from the first picture component, and the component model characterizing a mapping relationship for mapping a sample value of the first picture component to a sample value of the to-be-predicted picture component ; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component model to obtain a mapped value; and determining a predicted value of the to-be-predicted picture component of the current block according to the mapped value . 4. The decoder of claim 3, wherein determining the multiple first picture component reference values according to the first picture component reference value set comprises: selecting reference samples from the samples in the one or more neighboring lines at the top side of the current block according to two preset positions, and selecting reference samples from the samples in the one or more neighboring columns at the left side of the current block according to two preset positions; determining the multiple first picture component reference values according to the selected samples. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANIKA M BRUMFIELD whose telephone number is (571)270-3700. The examiner can normally be reached M-F 8:30 - 5 PM AWS. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Czekaj can be reached at 571-272-7327. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. SHANIKA M. BRUMFIELD Examiner Art Unit 2487 /SHANIKA M BRUMFIELD/Examiner, Art Unit 2487 /Dave Czekaj/Supervisory Patent Examiner, Art Unit 2487 Application/Control Number: 19/073,537 Page 2 Art Unit: 2487 Application/Control Number: 19/073,537 Page 3 Art Unit: 2487 Application/Control Number: 19/073,537 Page 4 Art Unit: 2487 Application/Control Number: 19/073,537 Page 5 Art Unit: 2487 Application/Control Number: 19/073,537 Page 6 Art Unit: 2487 Application/Control Number: 19/073,537 Page 7 Art Unit: 2487 Application/Control Number: 19/073,537 Page 8 Art Unit: 2487 Application/Control Number: 19/073,537 Page 9 Art Unit: 2487 Application/Control Number: 19/073,537 Page 10 Art Unit: 2487 Application/Control Number: 19/073,537 Page 11 Art Unit: 2487 Application/Control Number: 19/073,537 Page 12 Art Unit: 2487 Application/Control Number: 19/073,537 Page 13 Art Unit: 2487 Application/Control Number: 19/073,537 Page 14 Art Unit: 2487 Application/Control Number: 19/073,537 Page 15 Art Unit: 2487 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Prosecution Insights