Probability Adaptation Rate Adjustment for Video Entropy Coding
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Solution Overview
Problem
Existing video encoding and decoding technologies face challenges in achieving optimal compression efficiency due to limitations in probability estimation and adaptation in entropy coding.
Innovation Solution
The proposed solution involves a probability adaptation rate adjustment (PARA) process that allows for flexible adaptation of the adaptation rate in probability estimation, using fixed modeling on a per-symbol or per-symbol group basis, or based on predefined mapping rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional probability estimation is used in entropy coding, then the encoding process is simple, but compression efficiency is limited
Solution Approach 1:
The patent applies dynamics by making the probability estimation adaptive rather than static. The adaptivity rate parameter allows the probability model to dynamically adjust based on the number of symbols processed, enabling the system to optimize compression efficiency at different stages of encoding without requiring complex manual tuning.
Solution Approach 2:
The patent changes the parameter of adaptivity rate dynamically during encoding. By modifying the adaptivity rate parameter value based on the number of symbols, the system optimizes the balance between modeling accuracy and computational complexity, achieving improved compression efficiency without excessive device complexity.
2Measurement precision
If higher adaptivity rate is used, then probability estimation accuracy improves, but computational complexity increases
Solution Approach 1:
The system dynamically adjusts the adaptivity rate parameter based on the number of symbols processed. This allows high accuracy when needed (with more symbols) while maintaining lower complexity when fewer symbols are processed, resolving the contradiction between precision and computational complexity.
Solution Approach 2:
The patent implements periodic updates of probability estimates based on symbol counts. This periodic action allows the system to maintain accuracy through regular updates while controlling computational complexity by updating only when necessary, rather than continuously at maximum adaptivity.
3Productivity
If per-symbol modeling is implemented, then compression efficiency improves, but processing time increases
Solution Approach 1:
The patent applies local quality by allowing different adaptivity rates for different symbol types or groups. This enables optimized compression efficiency for specific symbol categories without requiring maximum processing time for all symbols uniformly, thus balancing efficiency gains with time consumption.
Solution Approach 2:
The system uses partial action by applying probability estimation with varying degrees of adaptivity based on symbol characteristics. Rather than fully processing every symbol with maximum adaptivity, the system applies appropriate levels of processing selectively, improving overall efficiency while reducing total processing time.
Data Source
AI summary
Systems and methods are configured for accessing data representing video content, the data comprising a set of one or more symbols each associated with a syntax element; performing a probability estimation, for encoding the data, comprising: for each symbol, obtaining, based on the syntax element for that symbol, an adaptivity rate parameter value, the adaptivity rate parameter value being a function of a number of symbols in the set of one or more symbols; updating the adaptivity rate parameter value as a function of an adjustment parameter value; and generating, based on the updated adaptivity rate parameter value, a probability value; generating a probability estimation; and encoding, based on the CDF of the probability estimation, the data comprising the set of one or more symbols for transmission.