Coefficient Group Context Modeling for Modular Video Coding
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Solution Overview
Problem
Current video encoding standards, such as H.264/AVC and developing MPEG-H, face inefficiencies in encoding and decoding residual video data due to the high percentage of data occupied by quantized transform coefficients, particularly in the encoding of significant-coefficient flags and coefficient levels, which hampers compression efficiency.
Innovation Solution
The implementation of multi-level significance maps and context derivation methods for determining context when encoding and decoding significant-coefficient flags and coefficient levels, allowing for more efficient processing by modifying the context neighborhood to reduce overhead and improve modular processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive context modeling is used for significant-coefficient flags, then encoding accuracy is improved, but decoding complexity and computational overhead increase
Solution Approach 1:
The transform unit is divided into multiple coefficient groups (e.g., 4x4 or 8x8 blocks), and context modeling is applied segmentarily to each group rather than to the entire transform unit. This segmentation allows the use of a limited, fixed context neighborhood within each group, reducing decoding complexity while maintaining adequate encoding accuracy through localized context adaptation.
2Measurement precision
If extensive context neighborhood is used for significant-coefficient flags, then encoding accuracy is improved, but memory requirements increase
Solution Approach 1:
A fixed, limited context neighborhood is defined for each coefficient group, using only nearby significant-coefficient flags within the same group (e.g., flags in the right column, bottom row, and diagonally adjacent positions). This local quality approach ensures that memory requirements are bounded and manageable, while still providing sufficient contextual information for accurate encoding within each localized region.
3Ease of manufacture
If multi-level significance maps are implemented, then modular processing is enhanced, but context determination overhead increases
Solution Approach 1:
The context neighborhood is pre-defined and fixed for each coefficient group, specifying exactly which nearby significant-coefficient flags should be used for context determination. This preliminary action eliminates the need for dynamic context neighborhood selection during decoding, reducing context determination overhead while maintaining the modular processing benefits of multi-level significance maps.
Data Source
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AI summary
Methods of encoding and decoding for video data are described for encoding or decoding coefficients for a transform unit. In particular, the significant-coefficient flags for a coefficient group are encoded and decoded based upon a context determination, and the context is determined based upon the values of neighboring flags. The neighborhood used to determine the context varies depending on whether the significant-coefficient flag to be encoded or decoded is in the right column or bottom row of the coefficient group or not. If it is in the right column or bottom row one of the alternative context neighborhoods is used to avoid relying on significant-coefficient flags in other coefficient groups except for the flags immediately adj acent the right border and bottom border of the coefficient group, and the flag diagonally to the lower-right.