Multiple-model local illumination compensation

An apparatus for processing video data comprising:

• one or more storage media configured to store video data;
• and
• one or more processors configured to:
• classify first luma samples of a neighboring luma block of a reference block and second luma samples of a neighboring luma block of a current block into a plurality of groups, wherein each one of the plurality of groups is each associated with one or more of a range of intensities or a range of positions, wherein, to classify, the one or more processors are configured to classify two or more luma samples using one or more of intensities of the two or more luma samples or positions of the two or more luma samples, and wherein, to classify, the one or more processors are configured to restrict the first luma samples of the neighboring luma block of the reference block and the second luma samples of the neighboring luma block of the current block in a group of the plurality of groups such that a number of samples in the group satisfies a specific number of samples;
• derive one or more local illumination compensation parameters for each group of the plurality of groups that are each associated with the one or more of the range of intensities or the range of positions to generate a plurality of local illumination compensation parameters for the current block;
• derive a plurality of linear models between the neighboring luma block of the reference block and the neighboring luma block of the current block using the plurality of local illumination compensation parameters for the current block;
• and
• generate a prediction block using the plurality of linear models.

An apparatus comprising:

• means for classifying first luma samples of a neighboring luma block of a reference block and second luma samples of a neighboring luma block of a current block into a plurality of groups, wherein each one of the plurality of groups is each associated with one or more of a range of intensities or a range of positions, wherein the means for classifying comprises means for classifying two or more luma samples using one or more of intensities of the two or more luma samples or positions of the two or more luma samples, and wherein the means for classifying comprises means for restricting the first luma samples of the neighboring luma block of the reference block and the second luma samples of the neighboring luma block of the current block in a group of the plurality of groups such that a number of samples in the group satisfies a specific number of samples;
• means for deriving one or more local illumination compensation parameters for each group of the plurality of groups that are each associated with the one or more of the range of intensities or the range of positions to generate a plurality of local illumination compensation parameters for the current block;
• means for deriving a plurality of linear models between the neighboring luma block of the reference block and the neighboring luma block of the current block using the plurality of local illumination compensation parameters for the current block;
• and
• means for generating a prediction block using the plurality of linear models.

A non-transitory computer-readable storage medium storing instructions that, when executed, cause one or more processors to:

• classify first luma samples of a neighboring luma block of a reference block and second luma samples of a neighoring luma block of a current block into a plurality of groups, wherein each one of the plurality of groups is each associated with one or more of a range of intensities or a range of positions, wherein, to classify, the instructions cause the one or more processors to classify two or more luma samples using one or more of intensities of the two or more luma samples or positions of the two or more luma samples, and wherein, to classify, the instructions further cause the one or more processors to restrict the first luma samples of the neighboring luma block of the reference block and the second luma samples of the neighboring luma block of the current block in a group of the plurality of groups such that a number of samples in the group satisfies a specific number of samples;
• derive one or more local illumination compensation parameters for each group of the plurality of groups that are each associated with the one or more of the range of intensities or the range of positions to generate a plurality of local illumination compensation parameters for the current block;
• derive a plurality of linear models between the neighboring luma block of the reference block and the neighboring luma block of the current block using the plurality of local illumination compensation parameters for the current block;
• and
• generate a prediction block using the plurality of linear models.