Group Context Entropy Coding for Polygon Mesh Compression
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Solution Overview
Problem
Existing entropy coding mechanisms for polygonal mesh compression are inefficient due to limited adaptive contexts and constrained scope, leading to suboptimal encoding optimization.
Innovation Solution
The method involves grouping input coefficients based on their range and employing tailored entropy coding strategies for each group, dynamically adjusting thresholds, and using flip sign encoding to reduce redundancy, while optimizing the maximum value for arithmetic coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single entropy coding strategy is used for all coefficients, then the encoding process is simple, but the compression efficiency is suboptimal
Solution Approach 1:
The patent divides the set of N coefficients into K coefficient groups based on statistical properties such as value ranges. Each group is then encoded using a tailored entropy coding strategy optimized for its specific characteristics, rather than applying a single uniform encoding approach to all coefficients. This segmentation allows the system to achieve better compression efficiency by adapting to the statistical variations within different coefficient groups.
2Adaptability or versatility
If adaptive contexts are updated dynamically during encoding, then the encoding adapts to local characteristics, but the computational overhead increases
Solution Approach 1:
The patent performs statistical analysis and coefficient grouping in advance, before the actual entropy encoding process. By pre-characterizing the coefficients and organizing them into groups with similar statistical properties, the system reduces the computational burden during the encoding phase while still maintaining adaptivity to local characteristics through the use of group-specific coding strategies.
3Adaptability or versatility
If the scope of adaptive contexts is expanded, then more encoding scenarios can be optimized, but the memory requirements increase
Solution Approach 1:
The patent maintains different adaptive contexts for different coefficient groups based on their specific statistical properties, rather than using a single global context for all coefficients. Each coefficient group has its own optimized entropy coding strategy and context model, allowing the system to adapt to local characteristics of different coefficient ranges while keeping memory requirements manageable by only maintaining contexts for the necessary number of groups.
Data Source
AI summary
A method of encoding performed by at least one processor including generating a set of N coefficients for a polygon mesh; splitting the set of N coefficients into K coefficient groups, each coefficient group from the K coefficient groups associated with an entropy coding strategy based on one or more properties of the polygon mesh; performing, to generate a set of encoded coefficients, entropy encoding on each coefficient group from the K coefficient groups in accordance with a respective entropy coding strategy; and generating a video bitstream including the set of encoded coefficients, in which N and K are positive integers.