Cross-Component Chroma Prediction Using Luma Histogram Weighting
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Solution Overview
Problem
Existing video coding methods, such as Versatile Video Coding (VVC/H.266), face challenges in accurately predicting chroma samples due to mismatched intensity distributions between reference luma and co-located luma samples, leading to poorly fitted model parameters and increased prediction errors.
Innovation Solution
An enhanced method for cross-component prediction is introduced, which involves determining a reference area for predicting chroma samples based on a ratio between normalized luminance histograms, deriving weights, and using filter coefficients to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cross-component prediction methods (CCLM/CCCM) are used, then chroma sample prediction can be performed, but prediction accuracy deteriorates due to intensity distribution mismatch between reference luma and co-located luma samples
Solution Approach 1:
The patent changes the parameter selection by computing weights based on the ratio of normalized histograms between reference luma samples and co-located luma samples. This transforms the uniform weighting approach into a distribution-aware weighting scheme, where weights are adjusted to match the intensity distributions, thereby improving prediction accuracy and model parameter fit quality
Solution Approach 2:
The patent introduces normalized histogram ratios as an intermediary mechanism to bridge the intensity distribution gap between reference luma samples and co-located luma samples. This intermediary computation of weights based on distribution ratios serves as a mediator that aligns the two different distributions before performing the cross-component prediction
2Ease of manufacture
If uniform weights are used for all reference samples, then computation is simplified, but prediction accuracy deteriorates when intensity distributions differ
Solution Approach 1:
The patent transitions from uniform parameter weights to distribution-based weights by computing the ratio of normalized histograms. This parameter change allows the system to adapt weights according to intensity distribution characteristics, improving prediction accuracy while maintaining computational feasibility through efficient histogram-based calculations
Data Source
AI summary
A method comprising: receiving an image block unit of a frame, the image block unit comprising samples in color channels comprising at least one chrominance channel and one luminance channel; reconstructing samples of said luminance channel of the image block unit; determining a reference area for predicting target samples of at least one color channel of the image block unit, wherein said reference area comprises one or more reference samples in a neighboring block in current color channel/frame, in the neighboring of a co-located block in reference color channel/frame; and/or inside the co-located block in reference color channel/frame; determining weights for predicting said target samples based on a ratio between a normalized luminance histogram in said reference area and a normalized luminance histogram co-locating said target samples; determining filter coefficients of a filter for said predicting based on the weights, the reference samples and a shape of the filter; and predicting samples of at least one color channel of the image block unit based on the samples of said luminance channel and the filter coefficients.