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 .
Information Disclosure Statement
The information disclosure statements (IDS), submitted on 12/26/2024 and 6/11/2026, are being considered by the examiner.
Objections
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, “the first syntax element indicates that one group is to be used”, “a second syntax element indicating whether to derive three or more parameters”, “third syntax element indicating whether to perform classification into two groups is decoded” must be shown or the feature(s) must be canceled from the claims 6-9. No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claims 1-3, 5-6, and 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang (WO Patent Application Publication WO 2024/124188), (“Wang”).
Regarding claim 1, Wang meets the claim limitations as follows:
A prediction image generation apparatus for generating a prediction image (Apparatus for cross-component prediction) [Wang: Title; Figs. 3, 7] of a chroma image by using a luma image (predicting each of the chroma sample values of a video block from the bitstream based on a plurality of corresponding luma sample values) [Wang: Abstract], the prediction image generation apparatus comprising (Apparatus for cross-component prediction) [Wang: Title; Figs. 3, 7]:a CCLM prediction parameter derivation circuit ((decoder circuitry) [Wang: para. 0073; Figs. 3, 7]; (cross-component linear model (CCLM)) [Wang: para. 0012] )) that derives a CCLM prediction parameter (deriving the CCCM parameters) [Wang: para. 0011] including a first weight, a second weight (The two weights, w0 and w1 are determined by the intra prediction mode) [Wang: para. 00207], and a first offset value by using a luma target pixel corresponding to a chroma pixel position to be predicted in a target block and an adjacent pixel to the luma target pixel (the application may be applied to a situation where an offset is selected for any of a luma component, a Cb chroma cornponent and a Cr chroma component according to any other of the luma component, the Cb chroma component and the Cr chroma component to modify said any component based on the selected offset) [Wang: para. 00076; Figs. 14, 30]; (Figure 14 is a diagram illustrating used neighboring reconstructed samples) [Wang: para. 0041; Fig. 14]); anda CCLM (cross-component linear model (CCLM)) [Wang: para. 0012] prediction filter circuit (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] that generates a prediction pixel value of a chroma target pixel image (generation the final predictor) [Wang: para. 00346] by using two luma pixels including the luma target pixel of the target block and the adjacent pixel, and the CCLM prediction parameter (For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20], wherein the CCLM (cross-component linear model (CCLM)) [Wang: para. 0012] prediction filter circuit (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] derives a prediction pixel value of the chroma target pixel (generation the final predictor) [Wang: para. 00346] from a sum of a product of the luma target pixel and the first weight, a product of the adjacent pixel to the luma target pixel and the second weight, and the first offset value (In a second embodiment, a fusion of chroma intra prediction modes is proposed, wherein the DM mode and the four default modes can be fused with the MMLM_LT mode as follows:
pred = (w0*pred0 + w1*pred1 + (1 « (shift - 1))) » shift
where pred0 is the predictor obtained by applying the non-LM mode, pred1 is the
predictor obtained by applying the MMLM_LT mode and pred is the final predictor of the current chroma block. The two weights, w0 and w1 are determined by the intra prediction mode of adjacent chroma blocks and shift is set equal to 2. Specifically, when the above and left adjacent blocks are both coded, with LM modes, {w0, w1} = {1, 3}; when the above and left adjacent blocks are both coded with non-LM modes, {w0, w1} = {3, 1}; otherwise, {w0, w1} = {2, 2}) [Wang: para. 00207; 00346] – Note: In the above equation. pred is the prediction pixel value of the chroma target pixel, w0 and w1 are the first weight and the second weight, pred0 and pred1 are luma target pixel. It is clear from the equation that pred is a sum of products of these term).
Regarding claim 2, Wang meets the claim limitations as set forth in claim 1. Wang further meets the claim limitations as follow.
wherein a position of the adjacent pixel is a pixel (x + 1, y) to a right of the luma target pixel (x, y) or a pixel (x, y+1) to a bottom of the luma target pixel (x, y) (the chroma sample to be predicted and its above/north (N), below/south (S), left/west (W) and right/east (E) neighbors as illustrated in Figure 17) [Wang: para. 00508; Figs. 13, 14, 17] – Note: It is clear from Fig. 17 that the coordinate of the target pixel C is (x, y), then the coordinate of the adjacent right pixel is (x+1, y), and the coordinate of the adjacent bottom pixel is (x, y+1).
Regarding claim 3, Wang meets the claim limitations as set forth in claim 1. Wang further meets the claim limitations as follow.
a parameter decoder (decoder) [Wang: para. 00308] that decodes an index indicating a position of an adjacent pixel to a luma target pixel corresponding to a chroma pixel position to be predicted in a target block (In one example, instead of explicitly signaling the selected filter shape index, the used direction oriented filter shape can be derived at decoder to save bit overhead) [Wang: para. 00308; Please see Fig. 17], wherein the CCLM (cross-component linear model (CCLM)) [Wang: para. 0012] prediction parameter derivation circuit that (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] keeps the number of CCLM prediction parameters constant regardless of the index (the constant parameters are set as:
na equals to 13, which value is tradeoff between data accuracy and computational cost
nA2 equals to 6, results in lookup table size as 64, table size can be further reduced to 32 by up-scaling A2 when bdepth(A2) < 6 (e.g. A2 < 32 ).
ntable equals to 15, results in 16 bits data representation of table elements.
nA1 is set as 15, to avoid product overflow and keep 16 bits multiplication) [Wang: para. 00158], and
derives the CCLM prediction parameter (cross-component linear model (CCLM)) [Wang: para. 0012] ; (cross-component linear model (CCLM)) [Wang: para. 0012]) by switching a position of the adjacent pixel according to the index (the filter shape and number of filter taps can be predefined or
signaled or switched in Sequence Parameter Set (SPS), Adaptation Parameter Set (APS), Picture Parameter Set (PPS), Picture Header (PH), Slice Header (SH), Region, CTU, CU, Sub block, or Sample level. A set of filter shape candidates can be predefined, and a selection on the set of filter shape candidates may be signaled or switched in SPS, APS, PPS, PEI, SH, Region, CTU, CU, Subblock, or Sample level Different components (e.g., U and V) may have different filter switch control. For example, a set of filter shape candidates (e.g., indicated by index 0-5)) [Wang: para. 00261], and the CCLM filter circuit (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] that derives a prediction pixel by switching the adjacent pixel according to the index. ((For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20]; (the filter shape and number of filter taps can be predefined or signaled or switched in Sequence Parameter Set (SPS), Adaptation Parameter Set (APS), Picture Parameter Set (PPS), Picture Header (PH), Slice Header (SH), Region, CTU, CU, Sub block, or Sample level. A set of filter shape candidates can be predefined, and a selection on the set of filter shape candidates may be signaled or switched in SPS, APS, PPS, PEI, SH, Region, CTU, CU, Subblock, or Sample level Different components (e.g., U and V) may have different filter switch control. For example, a set of filter shape candidates (e.g., indicated by index 0-5)) [Wang: para. 00261]).
Regarding claim 5, Wang meets the claim limitations as set forth in claim 3. Wang further meets the claim limitations as follow.
wherein the parameter decoder decodes the index from coded data of a sequence header, a slice header, or a CTU header (the filter shape and number of filter taps can be predefined or signaled or switched in Sequence Parameter Set (SPS), Adaptation Parameter Set (APS), Picture Parameter Set (PPS), Picture Header (PH), Slice Header (SH), Region, CTU, CU, Sub block, or Sample level. A set of filter shape candidates can be predefined, and a selection on the set of filter shape candidates may be signaled or switched in SPS, APS, PPS, PEI, SH, Region, CTU, CU, Subblock, or Sample level Different components (e.g., U and V) may have different filter switch control. For example, a set of filter shape candidates (e.g., indicated by index 0-5)) [Wang: para. 00261], and derives a flag indicating whether to perform CCLM prediction from coded data (sps _ cclm_enabled_flag) [Wang: para. 00143]; and the CCLM filter circuit ((one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] ; (the filter shape and number of filter taps can be predefined or signaled or switched in Sequence Parameter Set (SPS), Adaptation Parameter Set (APS), Picture Parameter Set (PPS), Picture Header (PH), Slice Header (SH), Region, CTU, CU, Sub block, or Sample level. A set of filter shape candidates can be predefined, and a selection on the set of filter shape candidates may be signaled or switched in SPS, APS, PPS, PEI, SH, Region, CTU, CU, Subblock, or Sample level Different components (e.g., U and V) may have different filter switch control. For example, a set of filter shape candidates (e.g., indicated by index 0-5)) [Wang: para. 00261]) that derives a prediction pixel of a chroma target pixel of the target block (For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20].
Regarding claim 6, Wang meets the claim limitations as follows:
A prediction image generation apparatus for generating a prediction pixel (Apparatus for cross-component prediction) [Wang: Title; Figs. 3, 7] of a chroma pixel by using a luma pixel (predicting each of the chroma sample values of a video block from the bitstream based on a plurality of corresponding luma sample values) [Wang: Abstract] corresponding to a chroma pixel position to be predicted in a target block and an adjacent pixel to the luma target pixel (the application may be applied to a situation where an offset is selected for any of a luma component, a Cb chroma cornponent and a Cr chroma component according to any other of the luma component, the Cb chroma component and the Cr chroma component to modify said any component based on the selected offset) [Wang: para. 00076; Figs. 14, 30], the prediction image generation apparatus comprising:a CCLM (cross-component linear model (CCLM)) [Wang: para. 0012] prediction parameter derivation circuit (decoder circuitry) [Wang: para. 0073; Figs. 3, 7] that derives a CCLM prediction parameter (determining a luma sample region and a chroma sample region to derive a convolutional cross-component model (CCCM); (cross-component linear model (CCLM)) [Wang: para. 0012]; anda CCLM (a convolutional cross-component model (CCCM)) [Wang: para. 0011] prediction filter circuit (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] that generates a prediction pixel (generation the final predictor) [Wang: para. 00346] of the chroma target pixel by using the luma target pixel, the adjacent pixel, and the CCLM prediction parameter (For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20]; (Figure 14 is a diagram illustrating used neighboring reconstructed samples) [Wang: para. 0041; Fig. 14]), wherein a first linear predictor that derives two parameters as a multiplication coefficient and a bias coefficient, and a second linear predictor configured to derive three or more parameters (For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20] – Note: αi filter coefficients, β: offset, N: filter taps are three or more parameters), and
a multi-model configured to classify a luma signal into a group (predicting the chroma sample by applying one of a plurality of linear prediction models corresponding to the classified sample group to the luma sample) [Wang: para. 0006] according to a magnitude of the pixel value (That is, a luma-chroma sample pair is classified by only considering the intensity of the luma sample) [Wang: para. 00241] and derive a plurality of types of the CCLM prediction parameters according to the classification (As discussed above, each neighboring/internal chroma sample and its corresponding luma sample may be referred to as a luma-chroma sample pair. The one or more thresholds are associated with intensities of neighboring/ internal luma samples. In this case, each of the multiple groups corresponds to a respective one of the plurality of linear models) [Wang: para. 00314], and a single model configured to derive one type of the CCLM prediction parameter (For example, as a exemplary classifier, horizontal gradients grnd_hor may be classified into a first group, which correspond to a first linear model, and vertical gradients grad_ver may be classified into a second group, which correspond to a second linear model. In one example, the horizontal luma patterns may be generated only once) [Wang: para. 00313], and
in a case that a luma signal is the multi-model, three or more parameters are not derived (When combining with MMLM classifier, the following operations may be
performed: classifying neighboring reconstructed luma-chroma sample pairs of the current video block into 2 groups based on Threshold; deriving different linear models for different groups, wherein the deriving process may be GLM simplified, i.e., with the above pre-operations to reduce the number of taps; classifying luma-chroma sample pairs inside the CU (internal luma-chroma sample pairs, wherein each of the internal luma-chroma sample pairs comprises an internal chroma sample value to be predicted with the derived linear model) into 2 groups similarly based on Threshold; applying different linear models to the reconstructed luma samples in different groups; and predicting chroma samples in the CU based on different classified linear models.
predc (i, j) = α1 • recL’(i, j) + β1 if recL’(i, j) <= Threshold
predc (i, j) = α2 • recL’(i, j) + β2 if recL’(i, j) > Threshold) [Wang: para. 00315] – Note: Wang discloses that when multi models are used, the model uses only two CCLM parameters, which are a single pair of αi and βi. There is no need for other CCLM parameters such as a shift and a second α); (To reduce the cross-component redundancy, a cross-component linear model (CCLM) prediction mode is used in the VVC, for which the chroma samples are predicted based on the reconstructed luma samples of the same CU by using a linear model as follows:
pred (i, j) = α • recL’(i, j) + β) [Wang: para. 00128; Equation 1]).
Regarding claim 10, Wang meets the claim limitations as follows:
A prediction image generation method for generating a prediction image (Method for cross-component prediction) [Wang: Title; Figs. 3, 7] of a chroma image by using a luma image (predicting each of the chroma sample values of a video block from the bitstream based on a plurality of corresponding luma sample values) [Wang: Abstract], the prediction image generation method comprising:deriving (deriving) [Wang: para. 0011] a CCLM prediction parameter (cross-component linear model (CCLM)) [Wang: para. 0012] including a first weight, a second weight (The two weights, w0 and w1 are determined by the intra prediction mode) [Wang: para. 00207], and a first offset value by using a luma target pixel corresponding to a chroma pixel position to be predicted in a target block and an adjacent pixel to the luma target pixel (the application may be applied to a situation where an offset is selected for any of a luma component, a Cb chroma component and a Cr chroma component according to any other of the luma component, the Cb chroma component and the Cr chroma component to modify said any component based on the selected offset) [Wang: para. 00076; Figs. 14, 30]; (Figure 14 is a diagram illustrating used neighboring reconstructed samples) [Wang: para. 0041; Fig. 14]); andgenerating a prediction pixel value of a chroma target pixel image ((chroma samples to be predicted) [Wang: para. 00245]; (generation the final predictor) [Wang: para. 00346]) by using two luma pixels including the luma target pixel of the target block and the adjacent pixel, and the CCLM prediction parameter (For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20], wherein deriving a prediction pixel value of the chroma target pixel (generation the final predictor) [Wang: para. 00346] from a sum of a product of the luma target pixel and the first weight, a product of the adjacent pixel to the luma target pixel and the second weight, and the first offset value (In a second embodiment, a fusion of chroma intra prediction modes is proposed, wherein the DM mode and the four default modes can be fused with the MMLM_LT mode as follows:
pred = (w0*pred0 + w1*pred1 + (1 « (shift - 1))) » shift
where pred0 is the predictor obtained by applying the non-LM mode, pred1 is the
predictor obtained by applying the MMLM_LT mode and pred is the final predictor of the current chroma block. The two weights, w0 and w1 are determined by the intra prediction mode of adjacent chroma blocks and shift is set equal to 2. Specifically, when the above and left adjacent blocks are both coded, with LM modes, {w0, w1} = {1, 3}; when the above and left adjacent blocks are both coded with non-LM modes, {w0, w1} = {3, 1}; otherwise, {w0, w1} = {2, 2}) [Wang: para. 00207; 00346] – Note: In the above equation. pred is the prediction pixel value of the chroma target pixel, w0 and w1 are the first weight and the second weight, pred0 and pred1 are luma target pixel. It is clear from the equation that pred is a sum of products of these terms).
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 of this title, 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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (WO Patent Application Publication 2024/124188), (“Wang”), in view of Li et al. (US Patent US 12,477,126 B2), (“Li”).
Regarding claim 1, Wang meets the claim limitations as follows:
A prediction image generation apparatus for generating a prediction image (Apparatus for cross-component prediction) [Wang: Title; Figs. 3, 7] of a chroma image by using a luma image (predicting each of the chroma sample values of a video block from the bitstream based on a plurality of corresponding luma sample values) [Wang: Abstract], the prediction image generation apparatus comprising:a CCLM prediction parameter derivation circuit ((decoder circuitry) [Wang: para. 0073; Figs. 3, 7]; (derive a convolutional cross-component model (CCCM); deriving the CCCM parameters) [Wang: para. 0011]) that derives a CCLM prediction parameter (deriving the CCCM parameters) [Wang: para. 0011] including a first weight, a second weight (The two weights, w0 and w1 are determined by the intra prediction mode) [Wang: para. 00207], and a first offset value by using a luma target pixel corresponding to a chroma pixel position to be predicted in a target block and an adjacent pixel to the luma target pixel (the application may be applied to a situation where an offset is selected for any of a luma component, a Cb chroma cornponent and a Cr chroma component according to any other of the luma component, the Cb chroma component and the Cr chroma component to modify said any component based on the selected offset) [Wang: para. 00076; Figs. 14, 30]; (Figure 14 is a diagram illustrating used neighboring reconstructed samples) [Wang: para. 0041; Fig. 14]); anda CCLM (a convolutional cross-component model (CCCM)) [Wang: para. 0011] prediction filter circuit (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] that generates a prediction pixel value of a chroma target pixel image (generation the final predictor) [Wang: para. 00346] by using two luma pixels including the luma target pixel of the target block and the adjacent pixel, and the CCLM prediction parameter (For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20], wherein the CCLM (a convolutional cross-component model (CCCM)) [Wang: para. 0011] prediction filter circuit (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] derives a prediction pixel value of the chroma target pixel (generation the final predictor) [Wang: para. 00346] from a sum of a product of the luma target pixel and the first weight, a product of the adjacent pixel to the luma target pixel and the second weight, and the first offset value (In a second embodiment, a fusion of chroma intra prediction modes is proposed, wherein the DM mode and the four default modes can be fused with the MMLM_LT mode as follows:
pred = (w0*pred0 + w1*pred1 + (1 « (shift - 1))) » shift
where pred0 is the predictor obtained by applying the non-LM mode, pred1 is the
predictor obtained by applying the MMLM_LT mode and pred is the final predictor of the current chroma block. The two weights, w0 and w1 are determined by the intra prediction mode of adjacent chroma blocks and shift is set equal to 2. Specifically, when the above and left adjacent blocks are both coded, with LM modes, {w0, w1} = {1, 3}; when the above and left adjacent blocks are both coded with non-LM modes, {w0, w1} = {3, 1}; otherwise, {w0, w1} = {2, 2}) [Wang: para. 00207; 00346] – Note: In the above equation. pred is the prediction pixel value of the chroma target pixel, w0 and w1 are the first weight and the second weight, pred0 and pred1 are luma target pixel. It is clear from the equation that pred is a sum of products of these term).
Regarding claim 2, Wang meets the claim limitations as set forth in claim 1. Wang further meets the claim limitations as follow.
wherein a position of the adjacent pixel is a pixel (x + 1, y) to a right of the luma target pixel (x, y) or a pixel (x, y+1) to a bottom of the luma target pixel (x, y) (the chroma sample to be predicted and its above/north (N), below/south (S), left/west (W) and right/east (E) neighbors as illustrated in Figure 17) [Wang: para. 00508; Figs. 13, 14, 17] – Note: It is clear from Fig. 17 that the coordinate of the target pixel C is (x, y), then the coordinate of the adjacent right pixel is (x+1, y), and the coordinate of the adjacent bottom pixel is (x, y+1).
Regarding claim 3, Wang meets the claim limitations as set forth in claim 1. Wang further meets the claim limitations as follow.
a parameter decoder (decoder) [Wang: para. 00308] that decodes an index indicating a position of an adjacent pixel to a luma target pixel corresponding to a chroma pixel position to be predicted in a target block (In one example, instead of explicitly signaling the selected filter shape index, the used direction oriented filter shape can be derived at decoder to save bit overhead) [Wang: para. 00308; Please see Fig. 17], wherein the CCLM (a convolutional cross-component model (CCCM)) [Wang: para. 0011] prediction parameter derivation circuit that (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] keeps the number of CCLM prediction parameters constant regardless of the index (the constant parameters are set as:
na equals to 13, which value is tradeoff between data accuracy and computational cost
nA2 equals to 6, results in lookup table size as 64, table size can be further reduced to 32 by up-scaling A2 when bdepth(A2) < 6 (e.g. A2 < 32 ).
ntable equals to 15, results in 16 bits data representation of table elements.
nA1 is set as 15, to avoid product overflow and keep 16 bits multiplication) [Wang: para. 00158], and
derives the CCLM prediction parameter (deriving the CCCM parameters) [Wang: para. 0011] by switching a position of the adjacent pixel according to the index (the filter shape and number of filter taps can be predefined or
signaled or switched in Sequence Parameter Set (SPS), Adaptation Parameter Set (APS), Picture Parameter Set (PPS), Picture Header (PH), Slice Header (SH), Region, CTU, CU, Sub block, or Sample level. A set of filter shape candidates can be predefined, and a selection on the set of filter shape candidates may be signaled or switched in SPS, APS, PPS, PEI, SH, Region, CTU, CU, Subblock, or Sample level Different components (e.g., U and V) may have different filter switch control. For example, a set of filter shape candidates (e.g., indicated by index 0-5)) [Wang: para. 00261], and the CCLM filter circuit (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] that derives a prediction pixel by switching the adjacent pixel according to the index. ((For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20]; (the filter shape and number of filter taps can be predefined or signaled or switched in Sequence Parameter Set (SPS), Adaptation Parameter Set (APS), Picture Parameter Set (PPS), Picture Header (PH), Slice Header (SH), Region, CTU, CU, Sub block, or Sample level. A set of filter shape candidates can be predefined, and a selection on the set of filter shape candidates may be signaled or switched in SPS, APS, PPS, PEI, SH, Region, CTU, CU, Subblock, or Sample level Different components (e.g., U and V) may have different filter switch control. For example, a set of filter shape candidates (e.g., indicated by index 0-5)) [Wang: para. 00261]).
In the same field of endeavor, Li explicitly discloses “generating prediction pixel values” as follows:
generates a prediction pixel value (generating pixel values) [Li: col. 42, line 61; Fig. 19].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Wang with Li to program the system to implement of Li’s method.
Therefore, the combination of Wang with Li will enable the system to reduce the average bitrate of video from the input to the output of the coding component to produce a coded representation of the video. [Li: col. 42, line 49-59].
Regarding claim 4, Wang meets the claim limitations as set forth in claim 1. Wang further meets the claim limitations as follow.
a parameter coder (encoder) [Wang: para. 00304] that codes an index indicating a position of an adjacent pixel to a luma target pixel corresponding to a chroma pixel position to be predicted in a target block (The four neighboring luma samples corresponding to the selected locations are obtained by a down-sampling operation and the obtained four neighboring luma samples are compared four times to find two larger values: x0A and x1A, and two smaller values: x0B and x1B. Chroma sample values corresponding to the two larger values and the two smaller values are denoted as y0A, y1A, y0B and y1B respectively. Then Xa, Xb, Ya and Yb are derived as:
Xa =( x0A + x1A + 1 ) >>1 ;
Xb =( x0B + x1B + 1 ) >>1;
Ya =( y0A + y1A + 1 ) >>1 ;
Yb =( y0B + y1B + 1 ) >>1) [Wang: para. 00130; Equation (2)], wherein the CCLM (cross-component linear model (CCLM)) [Wang: para. 0012] prediction parameter derivation circuit (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] that keeps the number of CCLM prediction parameters constant regardless of the index (the constant parameters are set as:
na equals to 13, which value is tradeoff between data accuracy and computational cost
nA2 equals to 6, results in lookup table size as 64, table size can be further reduced to 32 by up-scaling A2 when bdepth(A2) < 6 (e.g. A2 < 32 ).
ntable equals to 15, results in 16 bits data representation of table elements.
nA1 is set as 15, to avoid product overflow and keep 16 bits multiplication) [Wang: para. 00158], and derives the CCLM prediction parameters (deriving the CCCM parameters) [Wang: para. 0011] by switching a position of the adjacent pixel according to the index (the filter shape and number of filter taps can be predefined or signaled or switched in Sequence Parameter Set (SPS), Adaptation Parameter Set (APS), Picture Parameter Set (PPS), Picture Header (PH), Slice Header (SH), Region, CTU, CU, Sub block, or Sample level. A set of filter shape candidates can be predefined, and a selection on the set of filter shape candidates may be signaled or switched in SPS, APS, PPS, PEI, SH, Region, CTU, CU, Subblock, or Sample level Different components (e.g., U and V) may have different filter switch control. For example, a set of filter shape candidates (e.g., indicated by index 0-5)) [Wang: para. 00261], andthe CCLM (cross-component linear model (CCLM)) [Wang: para. 0012] filter circuit (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] that derives a prediction pixel of a chroma target pixel by switching the adjacent pixel according to the index ((For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20]; (the filter shape and number of filter taps can be predefined or signaled or switched in Sequence Parameter Set (SPS), Adaptation Parameter Set (APS), Picture Parameter Set (PPS), Picture Header (PH), Slice Header (SH), Region, CTU, CU, Sub block, or Sample level. A set of filter shape candidates can be predefined, and a selection on the set of filter shape candidates may be signaled or switched in SPS, APS, PPS, PEI, SH, Region, CTU, CU, Subblock, or Sample level Different components (e.g., U and V) may have different filter switch control. For example, a set of filter shape candidates (e.g., indicated by index 0-5)) [Wang: para. 00261]).
Wang does not explicitly disclose the following claim limitations (Emphasis added).
codes an index.
However in the same field of endeavor, Li explicitly discloses deficient claim limitations as follows:
codes an index (Indications of the category index may be further signaled.
[1] In one example, the indication of the category index may be firstly coded, followed by an index relative to the category if needed. [2] In one example, the indication of the category index may be coded after an index relative to the category, if needed) [Li: col. 23, line 35-424; Fig. 19].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Wang with Li to program the system to implement of Li’s method.
Therefore, the combination of Wang with Li will enable the system to reduce the average bitrate of video from the input to the output of the coding component to produce a coded representation of the video. [Li: col. 42, line 49-59].
Regarding claim 5, Wang meets the claim limitations as set forth in claim 3. Wang further meets the claim limitations as follow.
wherein the parameter decoder decodes the index from coded data of a sequence header, a slice header, or a CTU header (the filter shape and number of filter taps can be predefined or signaled or switched in Sequence Parameter Set (SPS), Adaptation Parameter Set (APS), Picture Parameter Set (PPS), Picture Header (PH), Slice Header (SH), Region, CTU, CU, Sub block, or Sample level. A set of filter shape candidates can be predefined, and a selection on the set of filter shape candidates may be signaled or switched in SPS, APS, PPS, PEI, SH, Region, CTU, CU, Subblock, or Sample level Different components (e.g., U and V) may have different filter switch control. For example, a set of filter shape candidates (e.g., indicated by index 0-5)) [Wang: para. 00261], and derives a flag indicating whether to perform CCLM prediction from coded data (sps _ cclm_enabled_flag) [Wang: para. 00143]; and the CCLM filter circuit ((one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] ; (the filter shape and number of filter taps can be predefined or signaled or switched in Sequence Parameter Set (SPS), Adaptation Parameter Set (APS), Picture Parameter Set (PPS), Picture Header (PH), Slice Header (SH), Region, CTU, CU, Sub block, or Sample level. A set of filter shape candidates can be predefined, and a selection on the set of filter shape candidates may be signaled or switched in SPS, APS, PPS, PEI, SH, Region, CTU, CU, Subblock, or Sample level Different components (e.g., U and V) may have different filter switch control. For example, a set of filter shape candidates (e.g., indicated by index 0-5)) [Wang: para. 00261]) that derives a prediction pixel of a chroma target pixel of the target block (For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20].
Regarding claim 6, Wang meets the claim limitations as follows:
A prediction image generation apparatus for generating a prediction pixel (Apparatus for cross-component prediction) [Wang: Title; Figs. 3, 7] of a chroma pixel by using a luma pixel (predicting each of the chroma sample values of a video block from the bitstream based on a plurality of corresponding luma sample values) [Wang: Abstract] corresponding to a chroma pixel position to be predicted in a target block and an adjacent pixel to the luma target pixel (the application may be applied to a situation where an offset is selected for any of a luma component, a Cb chroma cornponent and a Cr chroma component according to any other of the luma component, the Cb chroma component and the Cr chroma component to modify said any component based on the selected offset) [Wang: para. 00076; Figs. 14, 30], the prediction image generation apparatus comprising:a CCLM (cross-component linear model (CCLM)) [Wang: para. 0012] prediction parameter derivation circuit (decoder circuitry) [Wang: para. 0073; Figs. 3, 7] that derives a CCLM prediction parameter (determining a luma sample region and a chroma sample region to derive a convolutional cross-component model (CCCM); (deriving the CCCM parameters) [Wang: para. 0011]; (cross-component linear model (CCLM)) [Wang: para. 0012]); anda CCLM (cross-component linear model (CCLM)) [Wang: para. 0012] prediction filter circuit (one or more microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs)) [Wang: para. 00073; Fig. 35] that generates a prediction pixel of the chroma target pixel (generation the final predictor) [Wang: para. 00346] by using the luma target pixel, the adjacent pixel, and the CCLM prediction parameter (For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20]; (Figure 14 is a diagram illustrating used neighboring reconstructed samples) [Wang: para. 0041; Fig. 14]), wherein a first linear predictor that derives two parameters as a multiplication coefficient and a bias coefficient (To reduce the cross-component redundancy, a cross-component linear model (CCLM) prediction mode is used in the VVC, for which the chroma samples are predicted based on the reconstructed luma samples of the same CU by using a linear model as follows:
pred (i, j) = α • recL’(i, j) + β) [Wang: para. 00128; Equation 1]), and a second linear predictor configured to derive three or more parameters (For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20] – Note: αi filter coefficients, β: offset, N: filter taps are three or more parameters), and
a multi-model configured to classify a luma signal into a group (predicting the chroma sample by applying one of a plurality of linear prediction models corresponding to the classified sample group to the luma sample) [Wang: para. 0006] according to a magnitude of the pixel value (That is, a luma-chroma sample pair is classified by only considering the intensity of the luma sample) [Wang: para. 00241] and derive a plurality of types of the CCLM prediction parameters according to the classification (As discussed above, each neighboring/internal chroma sample and its corresponding luma sample may be referred to as a luma-chroma sample pair. The one or more thresholds are associated with intensities of neighboring/ internal luma samples. In this case, each of the multiple groups corresponds to a respective one of the plurality of linear models) [Wang: para. 00314], and a single model configured to derive one type of the CCLM prediction parameter (For example, as an exemplary classifier, horizontal gradients grnd_hor may be classified into a first group, which correspond to a first linear model, and vertical gradients grad_ver may be classified into a second group, which correspond to a second linear model. In one example, the horizontal luma patterns may be generated only once) [Wang: para. 00313], and
in a case that a luma signal is the multi-model, three or more parameters are not derived (When combining with MMLM classifier, the following operations may be
performed: classifying neighboring reconstructed luma-chroma sample pairs of the current video block into 2 groups based on Threshold; deriving different linear models for different groups, wherein the deriving process may be GLM simplified, i.e., with the above pre-operations to reduce the number of taps; classifying luma-chroma sample pairs inside the CU (internal luma-chroma sample pairs, wherein each of the internal luma-chroma sample pairs comprises an internal chroma sample value to be predicted with the derived linear model) into 2 groups similarly based on Threshold; applying different linear models to the reconstructed luma samples in different groups; and predicting chroma samples in the CU based on different classified linear models.
predc (i, j) = α1 • recL’(i, j) + β1 if recL’(i, j) <= Threshold
predc (i, j) = α2 • recL’(i, j) + β2 if recL’(i, j) > Threshold) [Wang: para. 00315] – Note: Wang discloses that when multi models are used, the model uses only two CCLM parameters, which are a single pair of αi and βi. There is no need for other CCLM parameters such as a shift and a second α); (To reduce the cross-component redundancy, a cross-component linear model (CCLM) prediction mode is used in the VVC, for which the chroma samples are predicted based on the reconstructed luma samples of the same CU by using a linear model as follows:
pred (i, j) = α • recL’(i, j) + β) [Wang: para. 00128; Equation 1]).
In the same field of endeavor, Li explicitly discloses “generating prediction pixel values” as follows:
generates a prediction pixel value (generating pixel values) [Li: col. 42, line 61; Fig. 19].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Wang with Li to program the system to implement of Li’s method.
Therefore, the combination of Wang with Li will enable the system to reduce the average bitrate of video from the input to the output of the coding component to produce a coded representation of the video. [Li: col. 42, line 49-59].
Regarding claim 7, Wang meets the claim limitations as set forth in claim 6. Wang further meets the claim limitations as follow.
a first syntax element (The video decoder 30 may receive the syntax elements) [Wang: para. 0097] indicate whether to perform classification (Different classifiers may be used to classify the neighboring luma samples (e.g., of the neighboring luma-chroma sample pairs) and/or the luma samples corresponding to chroma samples to be predicted. The luma samples corresponding to the chroma samples may be obtained by a down-sampling operation to match the locations of the corresponding chroma samples for 4:2:0 video sequences) [Wang: para. 00245] into two groups is decoded (For example, as an exemplary classifier, horizontal gradients grnd_hor may be classified into a first group, which correspond to a first linear model, and vertical gradients grad_ver may be classified into a second group, which correspond to a second linear model. In one example, the horizontal luma patterns may be generated only once) [Wang: para. 00313], and in a case that the first syntax element indicates that one group is to be used (The video decoder 30 may receive the syntax elements) [Wang: para. 0097], a second syntax element indicating whether to derive three or more parameters is decoded ((The linear weighted plus offset value directly forms the predicted chroma sample (can be low pass, high pass adaptively according to video content), and it is then added by the residual to form the reconstructed chroma sample) [Wang: para. 00253; Note: Equation 32-1 illustrates that only two parameters are used]; (In some implementation like CCCM, the offset term can also be implemented as middle chroma value B (512 for 10-bit content) multiplied by another coefficient, as shown in the following equation (32-2)) [Wang: para. 00254; Note: Equation 32-2 illustrates that three or more parameters are used]; (When combining with MMLM classifier, the following operations may be performed: classifying neighboring reconstructed luma-chroma sample pairs of the current video block into 2 groups based on Threshold; deriving different linear models for different groups, wherein the deriving process may be GLM simplified, i.e., with the above pre-operations to reduce the number of taps; classifying luma-chroma sample pairs inside the CU (internal luma-chroma sample pairs, wherein each of the internal luma-chroma sample pairs comprises an internal chroma sample value to be predicted with the derived linear model) into 2 groups similarly based on Threshold; applying different linear models to the reconstructed luma samples in different groups; and predicting chroma samples in the CU based on different classified linear models.
predc (i, j) = α1 • recL’(i, j) + β1 if recL’(i, j) <= Threshold
predc (i, j) = α2 • recL’(i, j) + β2 if recL’(i, j) > Threshold) [Wang: para. 00315] – Note: Wang discloses that when multi models are used, the model uses only two CCLM parameters, which are a single pair of αi and βi. There is no need for other CCLM parameters such as a shift and a second α); (To reduce the cross-component redundancy, a cross-component linear model (CCLM) prediction mode is used in the VVC, for which the chroma samples are predicted based on the reconstructed luma samples of the same CU by using a linear model as follows:
pred (i, j) = α • recL’(i, j) + β) [Wang: para. 00128; Equation 1];).
In the same field of endeavor, Li explicitly discloses syntax elements as follows:
a first syntax element, a second syntax element ({
if( Cclm Enabled)
cclm_mode_flag
if( cclm_mode_flag)
cclm_mode_idx)) [Li: col. 15, line 26-29; Fig. 19].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Wang with Li to program the system to implement of Li’s method.
Therefore, the combination of Wang with Li will enable the system to reduce the average bitrate of video from the input to the output of the coding component to produce a coded representation of the video. [Li: col. 42, line 49-59].
Regarding claim 8, Wang meets the claim limitations as set forth in claim 7. Wang further meets the claim limitations as follow.
wherein the second syntax element is decoded ((The video decoder 30 may receive the syntax elements) [Wang: para. 0097]; (Different classifiers may be used to classify the neighboring luma samples (e.g., of the neighboring luma-chroma sample pairs) and/or the luma samples corresponding to chroma samples to be predicted. The luma samples corresponding to the chroma samples may be obtained by a down-sampling operation to match the locations of the corresponding chroma samples for 4:2:0 video sequences) [Wang: para. 00245]), and
in a case that the second syntax element indicates to derive two parameters (The linear weighted plus offset value directly forms the predicted chroma sample (can be low pass, high pass adaptively according to video content), and it is then added by the residual to form the reconstructed chroma sample) [Wang: para. 00253; Equation 32-1], third syntax element indicating whether to perform classification into two groups is decoded (For example, as an exemplary classifier, horizontal gradients grnd_hor may be classified into a first group, which correspond to a first linear model, and vertical gradients grad_ver may be classified into a second group, which correspond to a second linear model. In one example, the horizontal luma patterns may be generated only once) [Wang: para. 00313].
In the same field of endeavor, Li explicitly discloses the second syntax element and the third syntax element as follows:
in a case that the second syntax element indicates to derive two parameters, third syntax element indicating whether to perform classification into two groups is decoded ({
if( Cclm Enabled)
cclm_mode_flag
if( cclm_mode_flag)
cclm_mode_idx)) [Li: col. 15, line 26-29; Fig. 19].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Wang with Li to program the system to implement of Li’s method.
Therefore, the combination of Wang with Li will enable the system to reduce the average bitrate of video from the input to the output of the coding component to produce a coded representation of the video. [Li: col. 42, line 49-59].
Regarding claim 9, Wang meets the claim limitations as set forth in claim 7. Wang further meets the claim limitations as follow.
in a case that the first syntax element (The video decoder 30 may receive the syntax elements) [Wang: para. 0097] indicates to perform classification (Different classifiers may be used to classify the neighboring luma samples (e.g., of the neighboring luma-chroma sample pairs) and/or the luma samples corresponding to chroma samples to be predicted. The luma samples corresponding to the chroma samples may be obtained by a down-sampling operation to match the locations of the corresponding chroma samples for 4:2:0 video sequences) [Wang: para. 00245] into two groups (For example, as an exemplary classifier, horizontal gradients grnd_hor may be classified into a first group, which correspond to a first linear model, and vertical gradients grad_ver may be classified into a second group, which correspond to a second linear model. In one example, the horizontal luma patterns may be generated only once) [Wang: para. 00313] and a size of the target block is less than a predetermined value ((predicting chroma samples in the CU based on different classified linear models.
predc (i, j) = α1 • recL’(i, j) + β1 if recL’(i, j) <= Threshold
predc (i, j) = α2 • recL’(i, j) + β2 if recL’(i, j) > Threshold) [Wang: para. 00315] – Note: Wang discloses that based on a comparison to a threshold, two different linear models can be used)), the prediction pixel is derived by using the first linear predictor ((To reduce the cross-component redundancy, a cross-component linear model (CCLM) prediction mode is used in the VVC, for which the chroma samples are predicted based on the reconstructed luma samples of the same CU by using a linear model as follows:
pred (i, j) = α • recL’(i, j) + β) [Wang: para. 00128; Equation 1]; (The linear weighted plus offset value directly forms the predicted chroma sample (can be low pass, high pass adaptively according to video content), and it is then added by the residual to form the reconstructed chroma sample) [Wang: para. 00253; Note: Equation 32-1 illustrates that only two parameters are used], andotherwise, the prediction pixel is derived by using the second linear predictor ((predicting chroma samples in the CU based on different classified linear models.
predc (i, j) = α1 • recL’(i, j) + β1 if recL’(i, j) <= Threshold
predc (i, j) = α2 • recL’(i, j) + β2 if recL’(i, j) > Threshold) [Wang: para. 00315] – Note: Wang discloses that based on a comparison to a threshold, two different linear models can be used); (In some implementation like CCCM, the offset term can also be implemented as middle chroma value B (512 for 10-bit content) multiplied by another coefficient, as shown in the following equation (32-2)) [Wang: para. 00254; Note: Equation 32-2 illustrates that three or more parameters are used].
In the same field of endeavor, Li explicitly discloses syntax elements as follows:
a first syntax element, a second syntax element ({
if( Cclm Enabled)
cclm_mode_flag
if( cclm_mode_flag)
cclm_mode_idx)) [Li: col. 15, line 26-29; Fig. 19].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Wang with Li to program the system to implement of Li’s method.
Therefore, the combination of Wang with Li will enable the system to reduce the average bitrate of video from the input to the output of the coding component to produce a coded representation of the video. [Li: col. 42, line 49-59].
Regarding claim 10, Wang meets the claim limitations as follows:
A prediction image generation method for generating a prediction image (Method for cross-component prediction) [Wang: Title; Figs. 3, 7] of a chroma image by using a luma image (predicting each of the chroma sample values of a video block from the bitstream based on a plurality of corresponding luma sample values) [Wang: Abstract], the prediction image generation method comprising:deriving (deriving) [Wang: para. 0011] a CCLM prediction parameter (deriving the CCCM parameters) [Wang: para. 0011] including a first weight, a second weight (The two weights, w0 and w1 are determined by the intra prediction mode) [Wang: para. 00207], and a first offset value by using a luma target pixel corresponding to a chroma pixel position to be predicted in a target block and an adjacent pixel to the luma target pixel (the application may be applied to a situation where an offset is selected for any of a luma component, a Cb chroma component and a Cr chroma component according to any other of the luma component, the Cb chroma component and the Cr chroma component to modify said any component based on the selected offset) [Wang: para. 00076; Figs. 14, 30]; (Figure 14 is a diagram illustrating used neighboring reconstructed samples) [Wang: para. 0041; Fig. 14]); andgenerating a prediction pixel value of a chroma target pixel image ((chroma samples to be predicted) [Wang: para. 00245]; (generation the final predictor) [Wang: para. 00346]) by using two luma pixels including the luma target pixel of the target block and the adjacent pixel, and the CCLM prediction parameter (For a to-be-predicted chroma sample, the reconstructed collocated and neighboring luma samples can be used to predict the chroma sample, to capture the inter-sample correlation among the collocated luma sample, neighboring luma samples, and the chroma sample. The reconstructed luma samples are linear weighted and combined with one "offset" to generate the predicted chroma sample (C: predicted chroma sample, Li. i-th reconstructed collocated or neighboring luma samples, αi filter coefficients, β: offset, N: filter taps), as shown in the following equation (32-1)) [Wang: para. 00253; Figs. 13-15A-B, 19-20], wherein deriving a prediction pixel value of the chroma target pixel (generation the final predictor) [Wang: para. 00346] from a sum of a product of the luma target pixel and the first weight, a product of the adjacent pixel to the luma target pixel and the second weight, and the first offset value (In a second embodiment, a fusion of chroma intra prediction modes is proposed, wherein the DM mode and the four default modes can be fused with the MMLM_LT mode as follows:
pred = (w0*pred0 + w1*pred1 + (1 « (shift - 1))) » shift
where pred0 is the predictor obtained by applying the non-LM mode, pred1 is the
predictor obtained by applying the MMLM_LT mode and pred is the final predictor of the current chroma block. The two weights, w0 and w1 are determined by the intra prediction mode of adjacent chroma blocks and shift is set equal to 2. Specifically, when the above and left adjacent blocks are both coded, with LM modes, {w0, w1} = {1, 3}; when the above and left adjacent blocks are both coded with non-LM modes, {w0, w1} = {3, 1}; otherwise, {w0, w1} = {2, 2}) [Wang: para. 00207; 00346] – Note: In the above equation. pred is the prediction pixel value of the chroma target pixel, w0 and w1 are the first weight and the second weight, pred0 and pred1 are luma target pixel. It is clear from the equation that pred is a sum of products of these terms).
In the same field of endeavor, Li explicitly discloses “generating prediction pixel values” as follows:
generates a prediction pixel value (generating pixel values) [Li: col. 42, line 61; Fig. 19].
It would have been obvious to one with an ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Wang with Li to program the system to implement of Li’s method.
Therefore, the combination of Wang with Li will enable the system to reduce the average bitrate of video from the input to the output of the coding component to produce a coded representation of the video. [Li: col. 42, line 49-59].
Reference Notice
Additional prior arts, included in the Notice of Reference Cited, made of record and not relied upon is considered pertinent to applicant's disclosure.
Contact Information
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