Transform Coefficient Coding Using Unified Context and Symbol Mapping
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Solution Overview
Problem
Existing image and video codecs face challenges in maintaining low complexity while achieving high coding efficiency, especially with varying block sizes and additional data components like depth maps, which require multiple contexts and symbolization schemes to adapt to coefficient statistics.
Innovation Solution
An apparatus and method that use a context-adaptive entropy decoder and symbolizer to map transform coefficients onto different symbolization schemes based on their levels, with a parameterizable function for context selection and symbolization parameter determination, allowing for efficient encoding and decoding of transform coefficients across different block sizes and information types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple contexts and symbolization schemes are used to adapt to coefficient statistics for varying block sizes and data components, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by using a single unified function that serves multiple purposes: it selects contexts for entropy coding and determines symbolization parameters for different block sizes and data components (luma, chroma, depth maps). This multi-functional approach replaces the need for separate contexts and symbolization schemes, achieving high coding efficiency while reducing device complexity.
Solution Approach 2:
The patent uses parameter changes by varying the function parameter (such as block size, component type) to adapt the unified function's behavior to different coding scenarios. Instead of creating separate contexts for each scenario, the function dynamically adjusts its operation based on the input parameters, maintaining coding efficiency while avoiding the complexity of multiple dedicated contexts.
2Device complexity
If a unified function is used for context adaptivity and symbolization parameter determination, then device complexity is reduced, but adaptability to different coding scenarios may be limited
Solution Approach 1:
The patent applies dynamics by making the unified function adaptive to different coding scenarios through dynamic parameter adjustment. The function responds to varying block sizes, data components, and coefficient statistics by adjusting its internal behavior based on the input parameters, ensuring high adaptability without requiring multiple static contexts or symbolization schemes.
Data Source
AI summary
An idea used herein is to use the same function for the dependency of the context and the dependency of the symbolization parameter on previously coded/decoded transform coefficients. Using the same function—with varying function parameter—may even be used with respect to different transform block sizes and/or frequency portions of the transform blocks in case of the transform coefficients being spatially arranged in transform blocks. A further variant of this idea is to use the same function for the dependency of a symbolization parameter on previously coded/decoded transform coefficients for different sizes of the current transform coefficient's transform block, different information component types of the current transform coefficient's transform block and/or different frequency portions the current transform coefficient is located within the transform block.