Cross-component prediction for video coding

An apparatus for video decoding, comprising:

• one or more processors;
• and
• one or more memory devices coupled to the one or more processors and configured to, individually or collectively, store instructions executable by the one or more processors,
• wherein the one or more processors, upon execution of the instructions, are configured to, individually or collectively, perform operations comprising:
• obtaining, from a video bitstream, a coding unit in a current picture, wherein the coding unit comprises a luma block and at least one chroma block;
• selecting a plurality of sets of neighboring samples of the coding unit, wherein each of the plurality of sets of neighboring samples comprises a neighboring chroma sample in a reference area and at least one neighboring luma sample corresponding to the neighboring chroma sample, wherein the reference area neighbors a chroma block of the at least one chroma block;
• determining one or more cross-component prediction models based on the plurality of sets of neighboring samples, wherein the one or more cross-component prediction models comprise at least one selected from a group that include a cross-component linear model (CCLM) and a multi-model linear model (MMLM);
• obtaining at least one reconstructed luma sample in the luma block that corresponds to a chroma sample in the at least one chroma block;
• and
• applying at least one of the one or more cross-component prediction models to the at least one reconstructed luma sample to predict the chroma sample.

A non-transitory computer-readable storage medium for video decoding storing a bitstream to be decoded by operations comprising:

• obtaining, from a video bitstream, a coding unit in a current picture, wherein the coding unit comprises a luma block and at least one chroma block;
• selecting a plurality of sets of neighboring samples of the coding unit, wherein each of the plurality of sets of neighboring samples comprises a neighboring chroma sample in a reference area and at least one neighboring luma sample corresponding to the neighboring chroma sample, wherein the reference area neighbors a chroma block of the at least one chroma block;
• determining one or more cross-component prediction models based on the plurality of sets of neighboring samples, wherein the one or more cross-component prediction models comprise at least one selected from a group that include a cross-component linear model (CCLM) and a multi-model linear model (MMLM);
• obtaining at least one reconstructed luma sample in the luma block that corresponds to a chroma sample in the at least one chroma block;
• and
• applying at least one of the one or more cross-component prediction models to the at least one reconstructed luma sample to predict the chroma sample.