Simplifications of cross-component linear model

Abstract

A computing device performs a method of decoding video data by reconstructing a luma block corresponding to a chroma block; searching a sub-group of a plurality of reconstructed neighboring luma samples in a predefined order to identify a maximum luma sample and a minimum luma sample; computing a down-sampled maximum luma sample corresponding to the maximum luma sample; computing a down-sampled minimum luma sample corresponding to the minimum luma sample; generating a linear model using the down-sampled maximum luma sample, the down-sampled minimum luma sample, the first reconstructed chroma sample, and the second reconstructed chroma sample; computing down-sampled luma samples from luma samples of the reconstructed luma block, wherein each down-sampled luma sample corresponds to a chroma sample of the chroma block; and predicting chroma samples of the chroma block by applying the liner model to the corresponding down-sampled luma samples.

What is claimed is: 
     
         1 . A method for decoding a video signal, comprising:
 reconstructing a luma block corresponding to a chroma block, wherein the luma block is adjacent to a plurality of reconstructed neighboring luma samples, and wherein the chroma block is adjacent to a plurality of reconstructed neighboring chroma samples;   computing at least one down-sampled luma sample from the plurality of reconstructed neighboring luma samples;   identifying, from a sub-group of the at least one computed down-sampled luma sample, at least one down-sampled maximum luma sample, wherein the at least one down-sampled maximum luma sample corresponds to a first reconstructed chroma sample of the plurality of reconstructed neighboring chroma samples;   identifying, from a sub-group of the at least one computed down-sampled luma sample, at least one down-sampled minimum luma sample, wherein the at least one down-sampled minimum luma sample corresponds to a second reconstructed chroma sample of the plurality of reconstructed neighboring chroma samples;   generating a linear model based on the at least one down-sampled maximum luma sample, the at least one down-sampled minimum luma sample, the first reconstructed chroma sample, and the second reconstructed chroma sample;   computing down-sampled luma samples from luma samples of the reconstructed luma block, wherein each down-sampled luma sample corresponds to a chroma sample of the chroma block; and   predicting chroma samples of the chroma block by applying the linear model to the corresponding computed down-sampled luma samples.   
     
     
         2 . The method of  claim 1 , wherein the chroma block and the luma block are encoded using a 4:2:0 or 4:2:2 chroma sub-sampling scheme, and wherein the chroma block and the luma block have different resolutions. 
     
     
         3 . The method of  claim 1 , wherein the chroma block and the luma block are encoded using a 4:4:4 chroma full sampling scheme, and wherein the chroma block and the luma block have the same resolution. 
     
     
         4 . The method of  claim 1 , wherein the plurality of reconstructed neighboring luma samples includes luma samples located above the reconstructed luma block and/or luma samples to left of the reconstructed luma block. 
     
     
         5 . The method of  claim 1 , wherein the computing down-sampled luma samples from luma samples of the reconstructed luma block comprises performing a weighted average of six neighboring luma samples to the luma sample. 
     
     
         6 . The method of  claim 1 , wherein the generating the linear model comprises fitting a linear equation through at least one data point associated with the at least one down-sampled maximum luma sample and the first reconstructed chroma sample and at least one data point associated with the at least one down-sampled minimum luma sample and the second reconstructed chroma sample. 
     
     
         7 . A non-transitory computer readable storage medium storing a plurality of programs for execution by a computing device having one or more processors, wherein the plurality of programs, when executed by the one or more processors, cause the computing device to perform operations including:
 reconstructing a luma block corresponding to a chroma block, wherein the luma block is adjacent to a plurality of reconstructed neighboring luma samples, and wherein the chroma block is adjacent to a plurality of reconstructed neighboring chroma samples;   computing at least one down-sampled luma sample from the plurality of reconstructed neighboring luma samples;   identifying, from a sub-group of the at least one computed down-sampled luma sample, at least one down-sampled maximum luma sample, wherein the at least one down-sampled maximum luma sample corresponds to a first reconstructed chroma sample of the plurality of reconstructed neighboring chroma samples;   identifying, from a sub-group of the at least one computed down-sampled luma sample, at least one down-sampled minimum luma sample, wherein the at least one down-sampled minimum luma sample corresponds to a second reconstructed chroma sample of the plurality of reconstructed neighboring chroma samples;   generating a linear model based on the at least one down-sampled maximum luma sample, the at least one down-sampled minimum luma sample, the first reconstructed chroma sample, and the second reconstructed chroma sample;   computing down-sampled luma samples from luma samples of the reconstructed luma block, wherein each down-sampled luma sample corresponds to a chroma sample of the chroma block; and   predicting chroma samples of the chroma block by applying the linear model to the corresponding computed down-sampled luma samples.   
     
     
         8 . The non-transitory computer readable storage medium of  claim 7 , wherein the chroma block and the luma block are encoded using a 4:2:0 or 4:2:2 chroma sub-sampling scheme, and wherein the chroma block and the luma block have different resolutions. 
     
     
         9 . The non-transitory computer readable storage medium of  claim 7 , wherein the chroma block and the luma block are encoded using a 4:4:4 chroma full sampling scheme, and wherein the chroma block and the luma block have the same resolution. 
     
     
         10 . The non-transitory computer readable storage medium of  claim 7 , wherein the plurality of reconstructed neighboring luma samples includes luma samples located above the reconstructed luma block and/or luma samples to left of the reconstructed luma block. 
     
     
         11 . The non-transitory computer readable storage medium of  claim 7 , wherein the computing down-sampled luma samples from luma samples of the reconstructed luma block comprises performing a weighted average of six neighboring luma samples to the luma sample. 
     
     
         12 . The non-transitory computer readable storage medium of  claim 7 , wherein the generating the linear model comprises fitting a linear equation through at least one data point associated with the at least one down-sampled maximum luma sample and the first reconstructed chroma sample and at least one data point associated with the at least one down-sampled minimum luma sample and the second reconstructed chroma sample. 
     
     
         13 . A method for decoding a video signal, comprising:
 reconstructing a luma block corresponding to a chroma block, wherein the luma block is adjacent to a plurality of reconstructed neighboring luma samples, and wherein the chroma block is adjacent to a plurality of reconstructed neighboring chroma samples;   computing one or more down-sampled luma samples from the plurality of reconstructed neighboring luma samples;   searching a sub-group of the one or more down-sampled luma samples in a predefined order to identify at least one down-sampled maximum luma sample and at least one down-sampled minimum luma sample, respectively, wherein the at least one down-sampled maximum luma sample corresponds to a first reconstructed chroma sample of the plurality of reconstructed neighboring chroma samples and the at least one down-sampled minimum luma sample corresponds to a second reconstructed chroma sample of the plurality of reconstructed neighboring chroma samples;   generating a linear model using the at least one down-sampled maximum luma sample, the at least one down-sampled minimum luma sample, the first reconstructed chroma sample, and the second reconstructed chroma sample;   computing down-sampled luma samples from luma samples of the reconstructed luma block, wherein each down-sampled luma sample corresponds to a chroma sample of the chroma block; and   predicting chroma samples of the chroma block by applying the linear model to the corresponding computed down-sampled luma samples.