DETAILED ACTION
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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5 and 8-16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by JHU et al (US 20250126284).
As to claim 1, JHU discloses a method of video coding for colour pictures using cross-component prediction (FIGS. 26-27), the method comprising:
receiving input data associated with a current block comprising a luma block and a chroma block (see [0436], [0458]), 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 (FIGS. 2-3);
deriving an auto-correlation matrix for reference luma samples in a reference area (see [0214]), wherein the auto-correlation matrix is associated with a footprint of a convolutional filter (see [0207]);
deriving a cross-correlation vector between the reference luma samples and reference chroma samples in the reference area (see [0214]), wherein the cross-correlation vector is associated with the footprint of the convolutional filter (see [0207]);
deriving coefficients of the convolutional filter based on the auto-correlation matrix and the cross-correlation vector using Gaussian elimination scheme (see [0212]-[0214], [0279], [0454]);
for a target chroma sample in the chroma block, generating a convolutional cross-component model predictor for the target chroma sample by applying the convolutional filter with the coefficients derived to a corresponding location of the luma block (see [0212]-[0213], predChromaVal; see [0176]-[0177], ChromaVal);
generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor (FIG. 12D and [0372]-[0374], final predictor; see [0440]); and
encoding or decoding the target chroma sample using the final predictor (see [0439]).
As to claim 2, JHU discloses an apparatus of video coding for colour pictures using cross-component prediction (FIG. 1), the apparatus comprising one or more electronics or processors arranged to (FIG. 28):
receive input data associated with a current block comprising a luma block and a chroma block (see [0436], [0458]), 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 (FIGS. 2-3);
derive an auto-correlation matrix for reference luma samples in a reference area (see [0214]), wherein the auto-correlation matrix is associated with a footprint of a convolutional filter (see [0207]);
derive a cross-correlation vector between the reference luma samples and reference chroma samples in the reference area (see [0214]), wherein the cross-correlation vector is associated with the footprint of the convolutional filter (see [0207]);
derive coefficients of the convolutional filter based on the auto-correlation matrix and the cross-correlation vector using Gaussian elimination scheme (see [0212]-[0214], [0279], [0454]);
for a target chroma sample in the chroma block, generate a convolutional cross-component model predictor for the target chroma sample by applying the convolutional filter with the coefficients derived to a corresponding location of the luma block (see [0212]-[0213], predChromaVal; see [0176]-[0177], ChromaVal);
generate a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor (FIG. 12D and [0372]-[0374], final predictor; see [0440]); and
encode or decode the target chroma sample using the final predictor (see [0439]).
As to claim 3, JHU discloses a method of video coding for colour pictures using cross-component prediction (FIGS. 26-27), the method comprising:
receiving input data associated with a current block comprising a luma block and a chroma block (see [0436], [0458]), 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 (FIGS. 2-3);
determining, depending on one or more conditions, a number of filter taps for a target convolutional filter (see [0415], One or more shape/number of filter taps may be used for CCCM prediction, as shown in FIG. 22, FIG. 23, FIG. 24 … One or more predefined shape/number of filter taps may be used for CCCM prediction based on previous decoded information on TB/CB/slice/picture/sequence level);
for a target chroma sample in the chroma block, generating a convolutional cross-component model predictor for the target chroma sample by applying the target convolutional filter to a corresponding location of the luma block (see [0212]-[0213], predChromaVal; see [0176]-[0177], ChromaVal);
generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor (FIG. 12D and [0372]-[0374], final predictor; see [0440]); and
encoding or decoding the target chroma sample using the final predictor (see [0439]).
As to claim 4, JHU further discloses wherein said one or more conditions correspond to one or more pre-defined implicit rules (see [0310], [0416]-[0417]; see [0443], in response to the block size being larger than or equal to the third value, selecting a CCCM; see [0453], determining (2606) the one or more cross-component prediction models comprises: determining at least one of filter parameters of a luma filter, the filter parameters comprising a filter shape and a number of filter taps of the luma filter).
As to claim 5, JHU further discloses wherein the target convolutional filter is determined implicitly according to a current block size (see [0443], [0453]).
As to claim 8, JHU further discloses wherein the target convolutional filter is selected from multiple convolutional filters with different numbers of filter taps (see [0415]-[0417]).
As to claim 9, JHU further discloses wherein the target convolutional filter is selected from multiple convolutional filters generated by separating a reference convolutional filter (FIGS. 21-25).
As to claim 10, JHU further discloses wherein one of said multiple convolutional filters achieving a best performance is explicitly signalled as the target convolutional filter (see [0416]).
As to claim 11, JHU further discloses wherein the target convolutional filter comprises an optional non-linear tap and a syntax is used to indicate whether the optional non-linear tap is used (see [0386]).
As to claim 12, JHU discloses an apparatus of video coding for colour pictures using cross-component prediction (FIG. 1), the apparatus comprising one or more electronics or processors arranged to (FIG. 28):
receive input data associated with a current block comprising a luma block and a chroma block (see [0436], [0458]), 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 (FIGS. 2-3);
determine, depending on one or more conditions, a number of filter taps for a target convolutional filter (see [0415], One or more shape/number of filter taps may be used for CCCM prediction, as shown in FIG. 22, FIG. 23, FIG. 24 … One or more predefined shape/number of filter taps may be used for CCCM prediction based on previous decoded information on TB/CB/slice/picture/sequence level);
for a target chroma sample in the chroma block, generate a convolutional cross-component model predictor for the target chroma sample by applying the target convolutional filter to a corresponding location of the luma block (see [0212]-[0213], predChromaVal; see [0176]-[0177], ChromaVal);
generate a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor (FIG. 12D and [0372]-[0374], final predictor; see [0440]); and
encode or decode the target chroma sample using the final predictor (see [0439]).
As to claim 13, JHU discloses a method of video coding for colour pictures using cross-component prediction (FIGS. 26-27), the method comprising:
receiving input data associated with a current block comprising a luma block and a chroma block (see [0436], [0458]), 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 (FIGS. 2-3);
signalling or parsing one or more syntax elements to indicate a target convolutional cross-component filter selected from multiple convolutional cross-component filters (see [0415], One or more shape/number of filter taps may be used for CCCM prediction, as shown in FIG. 22, FIG. 23, FIG. 24 … One or more predefined shape/number of filter taps may be used for CCCM prediction based on previous decoded information on TB/CB/slice/picture/sequence level; see [0416], the filter shape/number of filter taps can be predefined or signaled/switched in SPS/DPS/VPS/SEI/APS/PPS/PH/SH/Region/CTU/CU/Subblock/Sample level), wherein each convolutional cross-component filter uses at least two different luma samples from two different positions (FIG. 15 and [0416], top/left neighbouring reconstructed luma samples; see [0453]);
for a target chroma sample in the chroma block, generating a convolutional cross-
component model predictor for the target chroma sample by applying the target convolutional
cross-component filter to a corresponding location of the luma block (see [0212]-[0213], predChromaVal; see [0176]-[0177], ChromaVal);
generating a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor (FIG. 12D and [0372]-[0374], final predictor; see [0440]); and
encoding or decoding the target chroma sample using the final predictor (see [0439]).
As to claim 14, JHU further discloses wherein the target convolutional filter is selected from multiple convolutional filters with different shapes according to a block-level syntax (see [0416]).
As to claim 15, JHU further discloses wherein said one or more syntax elements are signalled or parsed at an SPS (Sequence Parameter Set), PPS (Picture Parameter Set), PH (Picture Header), SH (Slice Header), or CTU (Coding Tree Unit) level (see [0416]).
As to claim 16, JHU discloses an apparatus of video coding for colour pictures using cross-component prediction (FIG. 1), the apparatus comprising one or more electronics or processors arranged to:
receive input data associated with a current block comprising a luma block and a chroma block (see [0436], [0458]), 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 (FIGS. 2-3);
signal or parse one or more syntax elements to indicate a target convolutional cross-component filter selected from multiple convolutional cross-component filters (see [0415], One or more shape/number of filter taps may be used for CCCM prediction, as shown in FIG. 22, FIG. 23, FIG. 24 … One or more predefined shape/number of filter taps may be used for CCCM prediction based on previous decoded information on TB/CB/slice/picture/sequence level; see [0416], the filter shape/number of filter taps can be predefined or signaled/switched in SPS/DPS/VPS/SEI/APS/PPS/PH/SH/Region/CTU/CU/Subblock/Sample level), wherein each convolutional cross-component filter uses at least two different luma samples from two different positions (FIG. 15 and [0416], top/left neighbouring reconstructed luma samples; see [0453]);
for a target chroma sample in the chroma block, generate a convolutional cross-
component model predictor for the target chroma sample by applying the target convolutional
cross-component filter to a corresponding location of the luma block (see [0212]-[0213], predChromaVal; see [0176]-[0177], ChromaVal);
generate a final predictor for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor (FIG. 12D and [0372]-[0374], final predictor; see [0440]); and
encode or decode the target chroma sample using the final predictor (see [0439]).
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.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over JHU et al (US 20250126284) in view of ESER et al (US 20240212094).
As to claim 6, JHU fails to explicitly disclose wherein the target convolutional filter is derived by setting a derived coefficient of a reference convolutional filter to zero if the derived coefficient is smaller than a pre-defined threshold value.
However, ESER teaches wherein the target convolutional filter is derived by setting a derived coefficient of a reference convolutional filter to zero if the derived coefficient is smaller than a pre-defined threshold value (see [0024]).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify JHU using ESER’s teachings to derive the target convolutional filter by setting a derived coefficient of a reference convolutional filter to zero if the derived coefficient is smaller than a pre-defined threshold value in order to provide a filter with low computational complexity, low memory requirement, low power and low resource consumption and high operating frequency (ESER; [0007]).
Allowable Subject Matter
Claim 7 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOUBACAR ABDOU TCHOUSSOU whose telephone number is (571)272-7625. The examiner can normally be reached M-F 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chris Kelley can be reached at 5712727331. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BOUBACAR ABDOU TCHOUSSOU/Primary Examiner, Art Unit 2482
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