Context Tree Probability Modeling for Video Syntax Coding
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Solution Overview
Problem
Existing video compression schemes face challenges in determining appropriate probability models for entropy encoding and decoding, leading to inefficient entropy coding processes due to excessive group separation and imprecise probability estimation.
Innovation Solution
The use of a context tree to separate syntax elements based on context information, allowing for more accurate probability modeling and reduced computational costs by identifying optimal separation criteria and grouping syntax elements effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If syntax elements are separated into multiple groups using separation criteria, then probability modeling accuracy is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent segments syntax elements into multiple groups based on context information and separation criteria. Each group is assigned a specific probability model, allowing for more accurate probability estimation while managing complexity through structured organization of the segmentation process.
Solution Approach 2:
The patent applies different probability models to different groups of syntax elements based on their specific context information. This local quality approach ensures that each syntax element group is modeled with the most appropriate probability distribution, improving overall accuracy without uniformly increasing complexity across all elements.
2Measurement precision
If extensive separation criteria are applied to group syntax elements, then probability estimation accuracy is improved, but computational cost increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining separation criteria and context information categories before the actual encoding/decoding process. This allows the system to efficiently evaluate and apply appropriate probability models without performing extensive real-time computations, thereby reducing computational cost while maintaining accuracy.
Solution Approach 2:
The patent changes parameters by selecting different probability models based on context information and separation results. This parameter adaptation allows the system to achieve high probability estimation accuracy by matching the right model to the right syntax element group, avoiding the need for computationally expensive universal modeling approaches.
3Productivity
If context tree is used to organize syntax elements, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements a context tree structure where syntax elements are organized in nested groups based on context information. This nesting approach improves coding efficiency by allowing hierarchical processing and reuse of context information across different levels, while the structured nesting pattern helps manage the inherent complexity through systematic organization.
Data Source
AI summary
Video syntax elements are coded using a context tree. Context information used for coding previously-coded syntax elements is identified. A context tree is produced by separating the previously-coded syntax elements into data groups based on the context information. The context tree includes nodes representing the data groups. Separating the previously-coded syntax elements can include applying separation criteria against values of the context information to produce at least some of the nodes. Context information is then identified for another set of syntax elements to be coded. One of the nodes of the context tree is identified based on values of the context information associated with one of the other set of syntax elements. That syntax element is then coded according to a probability model associated with the identified node. The context tree can be used to encode or decode syntax elements.