DETAILED ACTION
Response to Arguments
Applicant’s arguments, see application, filed 05/12/2026, with respect to the claim objections and 112 rejections have been fully considered and are persuasive. The claim objections and 112 rejections have been withdrawn.
Applicant's arguments filed 05/12/2026 have been fully considered but they are not persuasive. Applicant has cancelled the limitations of claim 7 and incorporated these limitations into the independent claims. The arguments presented are not persuasive. Astola discloses weighted multi-model CCCM (Astola, para. 0241). Furthermore, Zhao discloses details of a weighted multi-model CCCM (Zhao, para. 0208, 0215 & 0234) and grouping luma samples into different groups and applying cross-component prediction modes to the first group and second group (Zhao, para. 0222-0225). Therefore, the weighted multi-model CCCM will generate multiple terms using different luma samples (i.e. applying CCCM to each group).
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Zhao et al. (herein after will be referred to as Zhao) (US 20240015279).
Regarding claim 1, Astola discloses
a method of video coding for colour pictures using cross-component prediction, the method comprising: [See Astola [0191] CCCM. Also, see Fig. 1, Codec (54).]
receiving input data associated with a current block comprising a luma block and a chroma block, wherein the input data comprise pixel data to be encoded at an encoder side or coded data associated with the current block to be decoded at a decoder side, and wherein the chroma block has a lower resolution than the luma block; [See Astola [0191] CCCM. Also, see Fig. 4a-4b for encoding/decoding. Also, see 0002, chroma has lower resolution than luminance.]
generating a down-sampled luma samples by applying a target down-sampling kernel to the luma block, wherein the target down-sampling kernel is selected from a filter set comprising multiple down-sampling kernels; [See Astola [Fig. 8 and 0192] The dimensions of the filter kernel include 1x3, 3x1, 3x3, 7x7 or any dimensions, and includes different shapes such as a cross, diamond, or any shape.]
determining a convolutional cross-component model predictor for a target chroma sample in the chroma block, wherein the convolutional cross-component model predictor comprises a term generated by applying a convolutional filter to a location of target down-sampled luma sample; [See Astola [0191-0200] CCCM (applicants background/related art further specifies that CCCM generates terms (Pg. 14 of the original specification)).]
generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor; and [See Astola [0191-0200] CCCM. Also, see 0241, Multi-model CCCM.]
encoding or decoding the target chroma sample using the final predictor. [See Astola [0191] CCCM. Also, see Fig. 4a/4b for encoding/decoding process using prediction.]
Astola does not explicitly disclose
wherein the convolutional cross-component model predictor comprises multiple terms generated by applying the convolutional filter to the location of target down-sampled luma sample using different down-sampled luma blocks samples.
However, Zhao does disclose
wherein the convolutional cross-component model predictor comprises multiple terms generated by applying the convolutional filter to the location of target down-sampled luma sample using different down-sampled luma blocks samples. [See Zhao [0234] Multiple prediction blocks of a current block are generated by multiple cross-component modes and a final prediction block is determined based on a weighted average. Also, see 0208, multi-model variant of CCCM. Also, see 0215, for multi-model CCCM, both models apply CCCM modes. Also, see 0222-0225, grouping luma samples into different groups and applying cross-component prediction modes to the first group and second group.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola to add the teachings of Zhao, in order to improve upon chroma prediction by taking a weighted average of multiple modes.
Regarding claim 2, Astola (modified by Zhao) disclose the method of claim 1. Furthermore, Astola discloses
wherein the multiple down-sampling kernels correspond to different filter coefficient sets. [See Astola [Fig. 8 and 0192] The dimensions of the filter kernel include 1x3, 3x1, 3x3, 7x7 or any dimensions, and includes different shapes such as a cross, diamond, or any shape. Also, see 0008, determining filter coefficients based on a shape of the filter.]
Regarding claim 3, Astola (modified by Zhao) disclose the method of claim 1. Furthermore, Astola discloses
wherein the multiple down-sampling kernels correspond to different filter shapes. [See Astola [Fig. 8 and 0192] The dimensions of the filter kernel include 1x3, 3x1, 3x3, 7x7 or any dimensions, and includes different shapes such as a cross, diamond, or any shape.]
Regarding claim 8, Astola (modified by Zhao) disclose the method of claim 1. Furthermore, Astola does not explicitly disclose
wherein the different down-sampled luma samples are generated by different target down-sampling filters from the filter set.
However, Zhao does disclose
wherein the different down-sampled luma samples are generated by different target down-sampling filters from the filter set. [See Zhao [0232] Different filters are applied for multiple groups of samples in a coding block.]
Applying the same motivation as applied in claim 1.
Regarding claim 9, see examiners rejection for claim 1 which is analogous and applicable for the rejection of claim 9.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Zhao (US 20240015279) and in further view of Laroche et al. (herein after will be referred to as Laroche) (US 20200389650).
Regarding claim 4, Astola (modified by Zhao) disclose the method of claim 1. Furthermore, Astola does not explicitly disclose
wherein the multiple down-sampling kernels are associated with multiple cross-component prediction modes.
However, Laroche does disclose
wherein the multiple down-sampling kernels are associated with multiple cross-component prediction modes. [See Laroche [0144] MMLM modes differ from each other by five different down-sampling filters.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola (modified by Zhao) to add the teachings of Laroche, in order to reduce the complexity of the derivation of the model parameters for the computation of chroma predictor [See Laroche [0020]].
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Zhao (US 20240015279) in view of Laroche (US 20200389650) and in further view of Choi et al. (herein after will be referred to as Choi) (US 20250267285).
Regarding claim 5, Astola (modified by Zhao and Laroche) disclose the method of claim 4. Furthermore, Astola does not explicitly disclose
wherein a best mode from the multiple cross-component prediction modes is signaled or parsed.
However, Choi does disclose
wherein a best mode from the multiple cross-component prediction modes is signaled or parsed. [See Choi [0223] Multi-model CCCM. Also, see 0163, Select an optimal mode and signal that mode.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola (modified by Zhao and Laroche) to add the teachings of Choi, in order to reduce the burden at the decoder-side by incorporating coding parameters determined at an encoder side.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Zhao (US 20240015279) in view of Laroche (US 20200389650) and in further view of Wang et al. (herein after will be referred to as Wang) (US 20240187576).
Regarding claim 6, Astola (modified by Zhao and Laroche) disclose the method of claim 4. Furthermore, Astola does not explicitly disclose
wherein a best mode from the multiple cross-component prediction modes is determined implicitly by comparing matching costs associated with the multiple cross-component prediction modes measured using one or more reference areas of the current block.
However, Wang does disclose
wherein a best mode from the multiple cross-component prediction modes is determined implicitly by comparing matching costs associated with the multiple cross-component prediction modes measured using one or more reference areas of the current block. [See Wang [0255] Derive the CCIP mode at the decoder using the reconstructed samples of neighboring blocks. Also, see 0260, all CCIP modes are listed in terms of a template cost and the mode with the minimum cost is selected as the derived mode. Also, see 0268, MMLM is derived implicitly.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola (modified by Zhao and Laroche) to add the teachings of Wang, in order to reduce bandwidth by implicitly determining coding parameters at the decoder side.
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Zhao (US 20240015279) and in further view of Jhu et al. (herein after will be referred to as Jhu) (US 20250126284).
Regarding claim 10, Astola discloses
a method of video coding for colour pictures using cross-component prediction, the method comprising: [See Astola [0191] CCCM. Also, see Fig. 1, Codec (54).]
receiving input data associated with a current block comprising a luma block and a chroma block, wherein the input data comprise pixel data to be encoded at an encoder side or coded data associated with the current block to be decoded at a decoder side, and wherein the chroma block has a lower resolution than the luma block; [See Astola [0191] CCCM. Also, see 0002, chroma has lower resolution than luminance.]
generating a down-sampled luma sample by applying a target down-sampling kernel to the luma block; [See Astola [Fig. 8 and 0192] The dimensions of the filter kernel include 1x3, 3x1, 3x3, 7x7 or any dimensions, and includes different shapes such as a cross, diamond, or any shape.]
determining a convolutional cross-component model predictor for a target chroma sample in the chroma block, wherein the convolutional cross-component model predictor comprises a term generated by applying a convolutional filter to a location of target down-sampled luma sample; [See Astola [0191] CCCM.]
generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor; and[See Astola [0191] CCCM.]
encoding or decoding the target chroma sample using the final predictor. [See Astola [0191] CCCM.]
Astola does not explicitly disclose
determining whether an enabling condition is satisfied, wherein the enabling condition comprises current block size; and in response to the enabling condition being satisfied:
wherein the convolutional cross-component model predictor comprises multiple terms generated by applying the convolutional filter to the location of target down-sampled luma sample using different down-sampled luma blocks samples.
However, Zhao does disclose
wherein the convolutional cross-component model predictor comprises multiple terms generated by applying the convolutional filter to the location of target down-sampled luma sample using different down-sampled luma blocks samples. [See Zhao [0234] Multiple prediction blocks of a current block are generated by multiple cross-component modes and a final prediction block is determined based on a weighted average. Also, see 0208, multi-model variant of CCCM. Also, see 0215, for multi-model CCCM, both models apply CCCM modes.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola to add the teachings of Zhao, in order to improve upon chroma prediction by taking a weighted average of multiple modes.
Astola (modified by Zhao) do not explicitly disclose
determining whether an enabling condition is satisfied, wherein the enabling condition comprises current block size; and in response to the enabling condition being satisfied:
However, Jhu does disclose
determining whether an enabling condition is satisfied, wherein the enabling condition comprises current block size; and in response to the enabling condition being satisfied: [See Jhu [0441] CCCM is only used for samples larger than or equal to a predefined number, such as the first value, for single model. For another example, FLM/GLM/ELM/CCCM is only used for samples larger than a predefined number, such as the second value, for multi model.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola (modified by Zhao) to add the teachings of Jhu, in order to improve upon coding efficiency [See Jhu [0002]].
Regarding claim 11, Astola (modified by Zhao and Jhu) disclose the method of claim 10. Furthermore, Astola does not explicitly disclose
wherein the current block size corresponds to current block width, current block height, or both.
However, Jhu does disclose
wherein the current block size corresponds to current block width, current block height, or both. [See Jhu [0362] Size restriction according to the CU area/width/height/depth.]
Applying the same motivation as applied in claim 10.
Regarding claim 12, Astola (modified by Zhao and Jhu) disclose the method of claim 10. Furthermore, Astola does not explicitly disclose
wherein the current block size corresponds to current block area.
However, Jhu does disclose
wherein the current block size corresponds to current block area. [See Jhu [0362] Size restriction according to the CU area/width/height/depth.]
Applying the same motivation as applied in claim 10.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Zhao (US 20240015279) in view of Jhu (US 20250126284) and in further view of Lim et al. (herein after will be referred to as Lim) (US 20220109846).
Regarding claim 13, Astola (modified by Zhao and Jhu) disclose the method of claim 10. Furthermore, Astola does not explicitly disclose
wherein the enabling condition is derived based on a logical AND or logical OR combination involving a current block width and current block height.
However, Lim does disclose
wherein the enabling condition is derived based on a logical AND or logical OR combination involving a current block width and current block height. [See Lim [0563] Comparing a threshold value with block area, block width, and/or block height for determining whether or not to perform CCLM prediction for a current block.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola (modified by Zhao and Jhu) to add the teachings of Lim, in order to utilize analogous teachings from cross-component prediction modes such as CCLM and apply it to a newer cross-component prediction mode such as CCCM.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Astola (US 20250373846) in view of Zhao (US 20240015279) in view of Jhu (US 20250126284) and in further view of Choi (herein after will be referred to as Choi ‘982) (US Patent No. 12,113,982).
Regarding claim 14, Astola (modified by Zhao and Jhu) disclose the method of claim 10. Furthermore, Astola does not explicitly disclose
wherein if an above line of the current block is across a CTU (Coding Tree Unit) row boundary, the enabling condition is not satisfied.
However, Choi ‘982 does disclose
wherein if an above line of the current block is across a CTU (Coding Tree Unit) row boundary, the enabling condition is not satisfied. [See Choi ‘982 [Equation 2 and 5, and Col. 14 lines 46-55] Downsampling is performed uses 6 reference samples and downsampling is performed using 3 reference samples when the CU crosses a CTU boundary.]
It would have been obvious to the person of ordinary skill in the art at the time of the effective filing date to modify the method by Astola (modified by Zhao and Jhu) to add the teachings of Choi ‘982, in order to improve upon coding efficiency [See Choi ‘982 [Col. 1 lines 42-43]].
Regarding claim 15, Astola (modified by Zhao, Jhu and Choi ‘982) disclose the method of claim 14. Furthermore, Astola does not explicitly disclose
wherein if the enabling condition is not satisfied, a shorter-tap convolutional filter is applied to generate the convolutional cross-component model predictor.
However, Choi ‘982 does disclose
wherein if the enabling condition is not satisfied, a shorter-tap convolutional filter is applied to generate the convolutional cross-component model predictor. [See Choi ‘982 [Equation 2 and 5, and Col. 14 lines 46-55, Downsampling is performed uses 6 reference samples and downsampling is performed using 3 reference samples when the CU crosses a CTU boundary.]
Applying the same motivation as applied in claim 14.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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