Transform Coefficient Block Coding With Partitioned Context Modeling
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Solution Overview
Problem
Current picture coding methods face inefficiencies in encoding and decoding transform coefficient blocks, particularly in using context-dependent entropy coding, where the dependency on other transform coefficients for context adaptation is not fully optimized, leading to suboptimal coding efficiency.
Innovation Solution
The proposed solution involves context-adaptive entropy encoding and decoding of transform coefficient blocks, where the context used for encoding or decoding a transform coefficient block depends on the transform coefficient block of a different color component, and specific contexts are used for distinct positions or partitions within the block, allowing for more precise probability estimation and improved coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If context-adaptive entropy encoding is used with dependency on transform coefficients of different color components, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The transform coefficient block is divided into multiple partitions (e.g., 2x2 blocks), and separate context sets are assigned to each partition. This segmentation allows the encoder to manage complexity by processing smaller independent units rather than the entire block at once, while still achieving improved coding efficiency through context-adaptive encoding across color components.
Solution Approach 2:
Different context sets are used for different partitions within the same transform coefficient block. Each partition can have its own context modeling, allowing the system to adapt to local characteristics of different regions while maintaining overall coding efficiency. This local differentiation reduces the need for a single complex context model.
2Measurement precision
If separate context sets are used for each partition of the transform coefficient block, then probability estimation is improved, but encoding complexity increases
Solution Approach 1:
The transform coefficient block is divided into multiple partitions (e.g., 2x2 blocks), and separate context sets are assigned to each partition. This segmentation allows the encoder to manage complexity by processing smaller independent units rather than the entire block at once, while still achieving improved coding efficiency through context-adaptive encoding across color components.
Solution Approach 2:
The patent changes the parameter of context modeling by using multiple discrete context sets (e.g., ContextSet0, ContextSet1, ContextSet2) instead of a single context model. This allows the system to adapt probability estimation to local characteristics of different partitions and color components, improving accuracy while keeping each individual context set relatively simple.
Data Source
AI summary
A video encoder is configured to entropy encode an absolute value of a quantization level of a current transform coefficient at position (xC, yC), wherein the absolute value is encoded using context adaptive binary arithmetic encoding of bins of a first binarization. The video coder is further configured, for the current transform coefficient, to encode a bin of the first binarization by using a context which is determined based on a sum of minimum absolute values of transform coefficient quantization levels, based on encoded bins of the first binarization, at one or more transform coefficient positions among (xC+1, yC), (xC+2, yC), (xC+1, yC+1), (xC, yC+1), and (xC, yC+2).