DETAILED ACTION
Response to Arguments
Applicant’s arguments with respect to claims 1-6 and 9-15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/07/2026 has been entered.
Specification
The disclosure is objected to because of the following informalities: The examiner could not locate equation 11 or an equation that is specified by equation 11. Please clarify. Appropriate correction is required.
Claim Rejections - 35 USC § 112
Claims 1-6, 9-15 and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention. It appears that the invention is directed to applying different down-sampling kernels associated with multiple CCCM modes and for selecting/indicating the best one/CCCM mode. However, the claims are broader and do not point out applicant’s novelty. For example, the limitation “generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor; and encoding or decoding the target chroma sample using the final predictor”. In regards to the specification, this should be a CCCM mode (i.e. the best mode). However, the claims can be interpreted that “a final predictor” is not a CCCM mode and makes the previous limitations unclear because these steps are being performed to generate a CCCM but then the encoder/decoder does not utilize it. For example, para. 0127 of the published specification states “The following method are proposed to improve the coding performance of CCCM”.
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 9 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo et al. (herein after will be referred to as Kuo) (US 20250097434) in view of Choi et al. (herein after will be referred to as Choi) (US 20250267285).
Regarding claim 1, Astola discloses
a method of video coding for colour pictures using cross-component prediction, the method comprising: [See Kuo [0146] CCCM.]
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 Kuo [0147] CCCM. Lower resolution chroma.]
applying a plurality of down-sampling kernels from a filter set to the luma block to generate a plurality of down-sampled luma blocks; [See Kuo [0267] Coefficient candidates for CCCM down-sampling. Applying different CCCM down-sample coefficients to different filter taps. Generating the down-sampled luma samples for CCCM input samples.]
determining a convolutional cross-component model predictor for a target chroma sample in the chroma block, wherein the convolutional cross-component model predictor is generated based on a convolutional cross-component model by applying a convolutional filter to a location of target down-sampled luma sample using different down-sampled luma blocks, and [See Kuo [0268-0275] Examples 1-5.]
wherein the different down-sampled luma blocks are generated by different target down-sampling kernels from the filter set, and wherein the convolutional cross-component model comprises the convolutional filter having multiple terms, wherein the multiple terms come from the different down-sampled luma blocks; [See Kuo [0268-0275] Examples 1-5.]
Kuo does not explicitly disclose
generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor; and
encoding or decoding the target chroma sample using the final predictor.
However, Choi does disclose
generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor; and [See Choi [0223] Multi-model CCCM. Also, see 0163, Select an optimal mode and signal that mode.]
encoding or decoding the target chroma sample using the final predictor. [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 Kuo to add the teachings of Choi, in order to improve upon CCCM by incorporating obvious prediction processes such as selecting a best prediction mode.
Regarding claim 2, Kuo (modified by Choi) disclose the method of claim 1. Furthermore, Kuo discloses
wherein the multiple down-sampling kernels correspond to different filter coefficient sets. [See Kuo [Figs. 15-16]]
Regarding claim 3, Kuo (modified by Choi) disclose the method of claim 1. Furthermore, Kuo discloses
wherein the multiple down-sampling kernels correspond to different filter shapes. [See Kuo [Figs. 15-16]]
Regarding claim 9, see examiners rejection for claim 1 which is analogous and applicable for the rejection of claim 9.
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Kuo (US 20250097434) in view of Choi (US 20250267285) and in further view of Laroche et al. (herein after will be referred to as Laroche) (US 20200389650).
Regarding claim 4, Kuo (modified by Choi) disclose the method of claim 1. Furthermore, Kuo 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 Kuo (modified by Choi) 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]].
Regarding claim 5, Kuo (modified by Choi and Laroche) disclose the method of claim 4. Furthermore, Kuo 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 Kuo to add the teachings of Choi, in order to improve upon CCCM by incorporating obvious prediction processes such as selecting a best prediction mode.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Kuo (US 20250097434) in view of Choi (US 20250267285) 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, Kuo (modified by Choi and Laroche) disclose the method of claim 4. Furthermore, Kuo 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 CCIP mode at the decoder using the reconstructed samples of neighboring blocks. Also, see 0260, all CCIP modes are listed in terms of 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 Kuo (modified by Choi 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 Kuo (US 20250097434) in view of Choi (US 20250267285) and in further view of Jhu et al. (herein after will be referred to as Jhu) (US 20250126284).
Regarding claim 10, Kuo discloses
a method of video coding for colour pictures using cross-component prediction, the method comprising: [See Kuo [0146] CCCM.]
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 Kuo [0147] CCCM. Lower resolution chroma.]
applying a plurality of down-sampling kernels from a filter set to the luma block to generate a plurality of down-sampled luma blocks; [See Kuo [0267] Coefficient candidates for CCCM down-sampling. Applying different CCCM down-sample coefficients to different filter taps. Generating the down-sampled luma samples for CCCM input samples.]
determining a convolutional cross-component model predictor for a target chroma sample in the chroma block, wherein the convolutional cross-component model predictor is generated based on a convolutional cross-component model by applying a convolutional filter to a location of target down-sampled luma sample using different down-sampled luma blocks, and [See Kuo [0268-0275] Examples 1-5.]
wherein the different down-sampled luma blocks are generated by different target down-sampling kernels from the filter set, and wherein the convolutional cross-component model comprises the convolutional filter having multiple terms, wherein the multiple terms come from the different down-sampled luma blocks; [See Kuo [0268-0275] Examples 1-5.]
Kuo 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:
generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor; and
encoding or decoding the target chroma sample using the final predictor.
However, Choi does disclose
generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor; and [See Choi [0223] Multi-model CCCM. Also, see 0163, Select an optimal mode and signal that mode.]
encoding or decoding the target chroma sample using the final predictor. [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 Kuo to add the teachings of Choi, in order to improve upon CCCM by incorporating obvious prediction processes such as selecting a best prediction mode.
Kuo (modified by Choi) 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 Kuo (modified by Choi) to add the teachings of Jhu, in order to improve upon coding efficiency [See Jhu [0002]].
Regarding claim 11, Kuo (modified by Choi and Jhu) disclose the method of claim 10. Furthermore, Kuo 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 [0366] Size restriction according to the CU area/width/height/depth.]
Applying the same motivation as applied in claim 10.
Regarding claim 12, Kuo (modified by Choi and Jhu) disclose the method of claim 10. Furthermore, Kuo 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 Kuo (US 20250097434) in view of Choi (US 20250267285) 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, Kuo (modified by Choi and Jhu) disclose the method of claim 10. Furthermore, Kuo 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 Kuo (modified by Choi 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 Kuo (US 20250097434) in view of Choi (US 20250267285) 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, Kuo (modified by Choi and Jhu) disclose the method of claim 10. Furthermore, Kuo 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 Kuo (modified by Choi 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, Kuo (modified by Choi, Jhu and Choi ‘982) disclose the method of claim 14. Furthermore, Kuo 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.
Allowable Subject Matter
Claim 17 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/JAMES T BOYLAN/Examiner, Art Unit 2486
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