Transform coefficient coding
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Solution Overview
Problem
Existing transform coefficient coding methods 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, increasing computational complexity.
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 parameterizable functions, allowing for adaptive encoding and decoding of transform coefficients across varying block sizes and components, using a unified function for context selection and symbolization parameter determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple contexts and symbolization schemes are used to handle varying block sizes and additional data components, then coding efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent applies a single unified function that serves multiple purposes: it selects contexts for entropy coding and determines symbolization parameters simultaneously. This multi-functional approach replaces the need for separate context selection and parameter determination mechanisms, thereby maintaining coding efficiency across varying block sizes and data components while reducing computational complexity.
Solution Approach 2:
The unified function takes as input the current transform coefficient level and previously coded transform coefficient levels, then dynamically outputs both the context index and symbolization parameter. By changing parameters based on the statistical properties of previously coded coefficients, the system adapts to different block sizes and data components without requiring multiple fixed schemes, thus improving efficiency while controlling complexity.
2Measurement precision
If a huge amount of differing symbolization schemes are used to adapt closely to actual statistics, then coding precision is improved, but device complexity increases
Solution Approach 1:
Instead of using a huge amount of different fixed symbolization schemes, the patent employs a dynamic parameter determination approach where the symbolization parameter is adaptively selected based on the statistical properties of previously coded transform coefficients. This dynamic adaptation allows the system to closely follow actual statistics and achieve high coding precision while avoiding the complexity of maintaining numerous separate schemes.
Solution Approach 2:
The system changes the symbolization parameter dynamically based on input from previously coded coefficients. By modifying the parameter according to actual statistical patterns rather than using fixed multiple schemes, the patent achieves precise adaptation to different data characteristics while keeping the underlying mechanism simple and unified.
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.