Context Modeling for Video Residual Coding Efficiency
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Solution Overview
Problem
Current video compression standards face inefficiencies in residual coding, particularly due to the complexity of context modeling in entropy coding, which affects compression efficiency and decoding complexity.
Innovation Solution
The method involves determining context model indices for quantized transform coefficients in different regions of a transform block, allowing for the selection of appropriate context models based on these indices to optimize entropy coding, thereby reducing unnecessary decoding steps and contexts in CABAC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a local neighboring template is used to model CABAC context for each quantized coefficient, then coding precision is improved, but device complexity increases due to the large number of contexts required
Solution Approach 1:
The transform block is divided into multiple regions, and different context modeling approaches are applied to different regions. Specifically, a local neighboring template is used for certain regions while a single context index is used for other regions, segmenting the problem to balance precision and complexity
Solution Approach 2:
Different parts of the transform block are assigned different context modeling strategies based on their characteristics. The patent applies local neighboring template modeling to regions where it provides significant benefit while using single context indexing in regions where it suffices, creating local quality variations to optimize the overall system
2Productivity
If multiple context models are used for different regions of the transform block, then coding efficiency is improved, but decoding complexity increases
Solution Approach 1:
The transform block is segmented into different regions with different context modeling approaches. This segmentation allows the system to achieve high coding efficiency in regions where multiple contexts are used while maintaining lower decoding complexity in regions where a single context suffices
Solution Approach 2:
Instead of applying full local neighboring template modeling to the entire transform block, the patent applies it only partially to specific regions where it provides the most benefit. This partial action reduces the overall decoding complexity while maintaining coding efficiency in critical areas
Data Source
AI summary
Different implementations are described, particularly implementations for video encoding and decoding using entropy coding of quantized transform coefficients. The method comprises: determining context model indices for one or more syntax elements of quantized transform coefficients of a transform block, wherein different context model indices are obtained for quantized transform coefficients in different regions of the transform block and a single context index is used for at least one region of the transform block; and selecting for the one or more syntax elements associated with a particular quantized transform coefficient one of several context models based on the determined content model index.