Cross-Component Residual Prediction for Chroma Coding Efficiency
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently compressing video data while maintaining quality, particularly in handling chroma samples, due to the limited utilization of cross-component redundancy between luma and chroma components.
Innovation Solution
The implementation of a residual template cross-component residual model (RT-CCRM) that generates residuals of chroma samples from associated luma samples, applying cross-component filtering to enhance coding efficiency by predicting chroma sample residuals using luma sample residuals, thereby facilitating local illumination compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional video coding methods are used to compress video data, then bandwidth and storage requirements are reduced, but chroma sample compression efficiency is insufficient due to limited utilization of cross-component redundancy
Solution Approach 1:
The patent introduces luma sample residuals as an intermediary to predict chroma sample residuals. By using the already-encoded luma residuals as a predictor for chroma residuals, the system exploits cross-component redundancy without requiring additional transmission of chroma residual data, thus improving compression efficiency while maintaining quality
Solution Approach 2:
The patent changes the parameter being encoded from direct chroma residuals to predicted chroma residuals based on luma residuals. This parameter transformation allows the system to exploit the correlation between luma and chroma components, achieving better compression ratios while maintaining reconstruction quality
2Productivity
If cross-component filtering is applied to predict chroma residuals from luma residuals, then coding efficiency is enhanced, but computational complexity increases
Solution Approach 1:
The patent applies cross-component filtering selectively rather than universally. By using syntax elements to indicate when RT-CCRM mode is enabled, the system applies the computationally intensive cross-component prediction only where beneficial, balancing coding efficiency improvements against computational complexity increases
Data Source
AI summary
The various implementations described herein include methods and systems for coding video. In one aspect, a video bitstream includes a current image frame having a current coding block and signals a first syntax element for a residual template cross-component residual model (RT-CCRM) mode. When the RT-CCRM mode is enabled, the computing system identifies, in the current coding block, a first chroma sample and one or more luma samples corresponding to the first chroma sample, determines one or more residuals of the one or more luma samples in the current coding block, and applies a residual filter corresponding to the RT-CCRM mode to generate a first residual of the first chroma sample based on the residuals of the one or more luma samples. The computing system reconstructs the current image frame by compensating a predicted chroma sample with at least the first residual to reconstruct the first chroma sample.