Significance Map Coding for Large Transform Coefficient Blocks
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Solution Overview
Problem
Conventional video coding methods face inefficiencies in coding large transform coefficient blocks due to increased computational overhead and inaccurate context modeling for significant coefficients, which existing methods fail to effectively address, especially for large blocks, particularly in the context of the entropy coding in H.264, where existing methods have not addressed the clustering of non-zero coefficients for large blocks, particularly in the context of the entropy coding in H.264.
Innovation Solution
The proposed solution is a context-adaptive entropy coding method for coding significant transform coefficients within the context of the entropy coding, which effectively addresses the clustering of non-zero coefficients in large blocks, utilizing a context-adaptive entropy coding method for large blocks, particularly in the context of the entropy coding in H.264.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional entropy coding methods are used for large transform coefficient blocks, then the coding process is simple, but the coding efficiency deteriorates due to increased computational overhead and inaccurate context modeling
Solution Approach 1:
The patent segments the transform coefficient block into multiple sub-blocks for independent processing. Each sub-block is coded separately with its own context modeling, which reduces the computational overhead compared to processing the entire large block as one unit while improving coding efficiency by capturing local coefficient characteristics more accurately.
Solution Approach 2:
The patent applies local context modeling where each position within the transform coefficient block uses context parameters adapted to local characteristics. The context model is updated based on previously decoded coefficients in the neighborhood, allowing the coding process to adapt to local patterns of significant coefficients rather than using a uniform global context model.
2Measurement precision
If conventional context modeling is used for significant coefficients, then the modeling process is simple, but the accuracy deteriorates for large blocks where non-zero coefficients are clustered
Solution Approach 1:
The patent performs preliminary identification of significant coefficients before applying context modeling. By first determining which coefficients are non-zero and their positions, the context model can be pre-adapted to the actual distribution pattern of significant coefficients in the block, improving accuracy by anticipating where non-zero values are likely to occur based on local patterns.
Solution Approach 2:
The patent implements feedback-based context modeling where the context parameters are continuously updated based on previously decoded coefficient values and their positions. The context model learns from the decoded sequence and adjusts its probability estimates for subsequent coefficients, improving accuracy by incorporating information from already decoded coefficients in the neighborhood.
Data Source
AI summary
A higher coding efficiency for coding a significance map indicating positions of significant transform coefficients within a transform coefficient block is achieved by the scan order by which the sequentially extracted syntax elements indicating, for associated positions within the transform coefficient block, as to whether at the respective position a significant or insignificant transform coefficient is situated, are sequentially associated to the positions of the transform coefficient block, among the positions of the transform coefficient block depends on the positions of the significant transform coefficients indicated by previously associated syntax elements. Alternatively, the first-type elements may be context-adaptively entropy decoded using contexts which are individually selected for each of the syntax elements dependent on a number of significant transform coefficients in a neighborhood of the respective syntax element, indicated as being significant by any of the preceding syntax elements.