Chroma Residual Prediction Using Luma Residual Templates
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Solution Overview
Problem
Existing video coding methods struggle to efficiently compress video data while maintaining high quality, particularly in applications with varying distortion tolerance levels, as they do not effectively utilize cross-component redundancy between luma and chroma samples.
Innovation Solution
The implementation of a residual template cross-component residual model (RT-CCRM) that predicts chroma sample residuals from associated luma sample residuals, using filter coefficients derived from templates, to enhance coding efficiency and compensate for local illumination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video coding methods are used, then video data can be compressed, but coding efficiency is insufficient and video quality degrades
Solution Approach 1:
The patent changes the parameter space by operating in the residual domain rather than the sample domain. By applying cross-component filtering to residuals (differences between predicted and actual chroma samples) rather than raw chroma samples, the method achieves better compression efficiency while maintaining video quality. The filter coefficients are derived from linear models that capture correlations between luma and chroma residuals, enabling more accurate prediction with fewer bits.
Solution Approach 2:
The patent introduces luma residuals as an intermediary to predict chroma residuals. Instead of directly compressing chroma data, the method uses the already-compressed luma residual information as a predictor for chroma residuals. This intermediary approach exploits the strong correlation between luma and chroma components, allowing the chroma residual to be predicted from luma residual with minimal additional bit rate, thereby improving overall coding efficiency.
2Manufacturing precision
If cross-component filtering is applied in sample domain, then chroma prediction is improved, but it cannot provide local illumination compensation
Solution Approach 1:
The patent transitions the operation domain from sample domain to residual domain. By applying cross-component filtering to residuals rather than samples, the method can capture local variations and illumination changes more effectively. The residual domain operation allows the filter to adapt to local illumination conditions because residuals represent local deviations from the prediction, enabling the filter to compensate for local illumination variations that sample-domain methods cannot capture.
3Productivity
If compression ratio is increased, then bandwidth and storage requirements are reduced, but distortion between original and reconstructed signals increases
Solution Approach 1:
The patent employs a feedback mechanism where the encoder and decoder both use the same residual template cross-component residual model (RT-CCRM) with identical filter coefficient derivation. The encoder computes chroma residuals using luma residuals and the RT-CCRM, then encodes only the difference. The decoder receives the encoded difference and reconstructs chroma residuals using the same RT-CCRM, ensuring bit-exact reconstruction. This feedback-aligned approach minimizes distortion while achieving high compression ratios by efficiently exploiting cross-component correlations.
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.