Context Model Indexing for Video Transform Coefficients
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently reducing context models for entropy coding of transform coefficients, leading to increased computational complexity and hardware requirements.
Innovation Solution
The method involves determining an offset value using a monotonic non-decreasing function on a sum of partially reconstructed transform coefficients, and then calculating a context model index based on this offset and a base value to select an appropriate context model for entropy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional context modeling is used for entropy coding of transform coefficients, then coding precision is maintained, but device complexity increases due to larger number of context models required
Solution Approach 1:
The transform coefficient block is divided into multiple regions based on coefficient positions, and different context modeling strategies are applied to different regions. This segmentation allows the system to use fewer context models overall while maintaining precision where it matters most (in significant coefficient regions) and accepting coarser modeling in less critical areas.
Solution Approach 2:
Different context modeling approaches are applied locally to different regions of the transform coefficient block. Significant coefficients (non-zero values) receive more precise context modeling with dedicated context models, while zero coefficients or less important regions use simplified or shared context models, optimizing the balance between precision and complexity.
2Device complexity
If the number of context models is reduced to decrease hardware complexity, then device complexity decreases, but coding efficiency may deteriorate
Solution Approach 1:
A limited set of context models is designed to serve multiple purposes and multiple coefficient positions. Each context model is crafted to be universally applicable across different regions and coefficient types, allowing the system to cover the entire transform coefficient block with fewer models while maintaining adequate coding efficiency through clever model design and selection.
Solution Approach 2:
The system applies context modeling selectively - using full precision context modeling only where absolutely necessary (for significant coefficients that most impact bitstream size) and applying simplified or no context modeling where it would provide minimal benefit. This partial action approach maintains overall coding efficiency while reducing the total number of context models required.
Data Source
AI summary
In a method of processing visual media data, a conversion is performed between a visual media file and a bitstream of visual media data according to a format rule. The bitstream includes at least one syntax element that corresponds to transform coefficients of a transform block in a current picture. The format rule specifies that a context model index is based on a sum of an offset value and a base value. The offset value is based on an output of a monotonically non-decreasing function performed on a sum of a group of partially reconstructed transform coefficients. A context model, for the at least one syntax of a current transform coefficient, from a plurality of context models is indicated by the context model index.