Significance Map Coding With Adaptive Scan Order and Contexts
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Solution Overview
Problem
Conventional video and image coding methods face inefficiencies in entropy coding of transform coefficient blocks, particularly for large blocks, due to suboptimal context modeling of significance maps, leading to increased computational overhead and reduced coding efficiency.
Innovation Solution
A method for decoding and encoding significance maps and transform coefficient blocks that adapts the scan order and context modeling based on the positions of significant transform coefficients, using context-adaptive entropy decoding and sub-block scanning to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional entropy coding is used for large transform coefficient blocks, then coding is performed, but computational overhead increases and coding efficiency decreases
Solution Approach 1:
The transform coefficient block is divided into multiple sub-blocks, and significance map syntax elements are coded separately for each sub-block. This segmentation reduces the computational overhead by breaking down the large block into smaller, more manageable units that can be processed independently with simpler context modeling.
Solution Approach 2:
Different context models are selected for different sub-blocks based on local characteristics such as the number of significant coefficients in each sub-block. This local adaptation improves coding efficiency by tailoring the probability estimates to the specific content of each region rather than using a single global context model.
2Adaptability or versatility
If fixed scan order is used for coding significance maps, then coding is performed, but adaptability to clustered significant coefficients is poor
Solution Approach 1:
The scan order is made dynamic and adaptive based on the actual distribution of significant coefficients in the transform block. The encoder determines the optimal scan order (e.g., horizontal, vertical, or diagonal) based on the clustering pattern of significant coefficients, allowing the coding process to adapt to different coefficient distributions rather than using a fixed predetermined order.
Solution Approach 2:
The scan order parameters are changed based on the characteristics of the transform coefficient block. Different scan directions and patterns are selected depending on where the significant coefficients are clustered, optimizing the coding efficiency for each specific case by matching the scan order to the coefficient distribution pattern.
3Productivity
If context-adaptive entropy decoding with neighborhood-based context selection is used, then coding efficiency improves, but decoding complexity increases
Solution Approach 1:
Instead of considering all possible neighborhood positions, the context selection focuses on a limited set of key neighborhood positions (e.g., immediate horizontal and vertical neighbors). This partial action approach provides sufficient adaptability to local coefficient distributions while keeping the decoding complexity manageable by not exhaustively analyzing every possible context.
Solution Approach 2:
The context model parameters are dynamically changed based on the number of significant coefficients detected in the neighborhood. The decoder adapts the probability estimates by adjusting context parameters according to local statistics, improving coding efficiency while maintaining a controlled level of complexity through parameter adaptation rather than full contextual analysis.
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.