Video Chroma Prediction With Multiple Cross-Component Residual Models
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Solution Overview
Problem
Existing video coding standards face challenges in efficiently predicting chroma samples based on luma samples, which affects compression efficiency and quality.
Innovation Solution
Implementing cross-component residual models (CCRMs) to predict chroma samples based on collocated luma samples during encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video coding standards are used for compressing video data, then storage memory and transmission bandwidth are reduced, but chroma sample prediction accuracy is insufficient
Solution Approach 1:
The patent divides the chroma prediction process into multiple segments by applying different CCRM models to different regions or conditions within a video block. This segmentation allows each region to be predicted with the most appropriate model, improving overall prediction accuracy without uniformly increasing complexity across the entire block.
Solution Approach 2:
The patent introduces dynamic model selection where the encoder and decoder adaptively choose among multiple CCRM models based on local characteristics of the video content. This dynamic approach allows the system to optimize prediction accuracy for each specific region while maintaining efficient compression by not using complex models everywhere.
2Reliability
If multiple cross-component residual models (CCRMs) are applied to predict chroma samples, then prediction accuracy and quality are improved, but encoding and decoding complexity increases
Solution Approach 1:
The patent applies different levels of model complexity to different regions based on local video characteristics. In regions where high prediction accuracy is critical, more sophisticated CCRM models are used, while in simpler regions, lighter models suffice. This local quality approach maintains overall video coding quality while reducing average complexity.
Solution Approach 2:
The patent changes key parameters of the CCRM models adaptively, such as adjusting the number of models applied, the complexity of each model, or the regions they cover, based on local video content analysis. This parameter adaptation allows the system to maintain high reliability where needed while controlling complexity elsewhere.
Data Source
AI summary
Methods for encoding a video sequence into a bitstream and decoding a bitstream to output one or more pictures for a video stream. An exemplary method includes: receiving a video sequence; encoding one or more pictures of the video sequence; and generating a bitstream associated with the encoded pictures, wherein the encoding comprises: predicting chroma samples within a current block based on luma samples corresponding to the chroma samples by a plurality of cross-component residual models (CCRMs).