# US 12,003,739 B2 · Cross component intra prediction mode > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,003,739 B2 - Document ID: US12003739B2 - Status: Granted - Kind: B2 - Application number: 18/078,285 - Filed: 2022-12-09 - Published: 2023-04-06 - Granted: 2024-06-04 - Earliest claimed: 2020-06-30 - Assignees: Tencent America LLC - Applicants: Tencent America LLC - Inventors: Liang Zhao; Xin Zhao; Shan Liu - Web page: https://patentgrep.com/patent/US12003739B2 ## CPC - H04N 19/11: among a plurality of spatial predictive coding modes - H04N 19/186: the unit being a colour or a chrominance component - H04N 19/136: Incoming video signal characteristics or properties - H04N 19/159: Prediction type, e.g. intra-frame, inter-frame or bidirectional frame prediction - H04N 19/172: the region being a picture, frame or field - H04N 19/176: the region being a block, e.g. a macroblock - H04N 19/182: the unit being a pixel - H04N 19/46: Embedding additional information in the video signal during the compression process (H04N19/517, H04N19/68, H04N19/70 take precedence) - H04N 19/593: involving spatial prediction techniques - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract A method, computer program, and computer system are provided for encoding video data. Luma and chroma samples are identified from image data corresponding to a video frame. In particular, at least one chroma sample may be predicted from at least one luma sample based on a DC contribution from a chroma component of the image data and an AC contribution from a luma component of the image data. Also, a plurality of linear models may be determined from the identified luma and chroma samples. The luma and chroma samples may be classified into a number of categories corresponding to a number of linear models, where each category has at least one corresponding linear model parameters signaled to the bitstream; the chroma sample prediction may be further based on such parameters. ## Claims ### Claim 1 A method of video encoding, executable by a processor, the method comprising: identifying at least one luma sample and at least one chroma sample from image data, wherein the at least one chroma sample is predicted from the at least one luma sample based on a DC contribution from a chroma component of the image data and an AC contribution from a luma component of the image data, wherein the AC contribution from the luma component is determined based on a subsampling of reconstructed luma pixels and at least one average luma value. ### Claim 2 (depends on 1) The method of claim 1, further comprising: determining a plurality of linear models from the identified at least one luma sample and the identified at least one chroma sample, wherein the at least one chroma sample is predicted from the at least one luma sample further based on a parameter of one of the plurality of linear models. ### Claim 3 (depends on 2) The method of claim 2, wherein each of the plurality of linear models respectively corresponds to a category of a plurality of categories, wherein the at least one luma sample and the at least one chroma sample are classified into one of the plurality of categories, and wherein each of the plurality of categories has at least one corresponding linear model parameter. ### Claim 4 (depends on 3) The method of claim 3, wherein each chroma sample is classified based on coordinates associated therewith. ### Claim 5 (depends on 3) The method of claim 3, wherein each chroma sample is classified based on a value of a corresponding reconstructed luma sample. ### Claim 6 (depends on 3) The method of claim 3, wherein the AC contribution from the luma component is further determined based on a plurality of average luma values, each average luma value corresponding to one of the plurality of categories and determined based on at least one luma sample classified into the corresponding category. ### Claim 7 (depends on 1) The method of claim 1, wherein at least one luma sample outside of boundaries associated with the image data is omitted in determining the at least one average luma value. ### Claim 8 (depends on 1) The method of claim 1, wherein the at least one average luma value is determined based on a luma sample in at least one predefined position in the image data. ### Claim 9 (depends on 1) The method of claim 1, wherein the at least one chroma sample is predicted from the at least one luma sample further based on a parameter of a linear model between luma and chroma samples. ### Claim 10 A computer system for encoding video data, the computer system comprising: at least one computer-readable non-transitory storage medium configured to store computer program code; and at least one computer processor configured to access said computer program code and operate as instructed by said computer program code, said computer program code comprising: identifying code configured to cause the at least one computer processor to identify at least one luma sample and at least one chroma sample from image data, wherein the at least one chroma sample is predicted from the at least one luma sample based on a DC contribution from a chroma component of the image data and an AC contribution from a luma component of the image data, wherein the AC contribution from the luma component is determined based on a subsampling of reconstructed luma pixels and at least one average luma value. ### Claim 11 (depends on 10) The computer system of claim 10, wherein the computer program code further comprises: determining code configured to cause the at least one computer processor to determine a plurality of linear models from the identified at least one luma sample and the identified at least one chroma sample, wherein the at least one chroma sample is predicted from the at least one luma sample further based on a parameter of one of the plurality of linear models. ### Claim 12 (depends on 11) The computer system of claim 11, wherein each of the plurality of linear models respectively corresponds to a category of a plurality of categories, wherein the at least one luma sample and the at least one chroma sample are classified into one of the plurality of categories, and wherein each of the plurality of categories has at least one corresponding linear model parameter. ### Claim 13 (depends on 12) The computer system of claim 12, wherein each chroma sample is classified based on coordinates associated therewith. ### Claim 14 (depends on 12) The computer system of claim 12, wherein the AC contribution from the luma component is further determined based on a plurality of average luma values, each average luma value corresponding to one of the plurality of categories and determined based on at least one luma sample classified into the corresponding category. ### Claim 15 (depends on 14) The computer system of claim 14, wherein at least one luma sample outside of boundaries associated with the image data is omitted in determining the at least one average luma value. ### Claim 16 (depends on 10) The computer system of claim 10, wherein the at least one average luma value is determined based on a luma sample in at least one predefined position in the image data. ### Claim 17 (depends on 10) The computer system of claim 10, wherein the at least one chroma sample is predicted from the at least one luma sample further based on a parameter of a linear model between luma and chroma samples. ### Claim 18 A non-transitory computer readable medium having stored thereon a computer program for encoding video data, the computer program configured to cause at least one computer processor to: identify at least one luma sample and at least one chroma sample from image data, wherein the at least one chroma sample is predicted from the at least one luma sample based on a DC contribution from a chroma component of the image data and an AC contribution from a luma component of the image data, wherein the AC contribution from the luma component is determined based on a subsampling of reconstructed luma pixels and at least one average luma value.