Entropy Context Initialization for Low-Complexity Video Coding
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Solution Overview
Problem
Existing video codecs face challenges in optimizing context-adaptive binary entropy coding, as the number of contexts can be too high or too low, leading to inefficient probability estimation and increased implementation complexity.
Innovation Solution
Implement a decoder and encoder that distinguish between 126 probability states using a linear equation for context initialization, deriving a slope and offset from the first and second four-bit parts of an 8-bit initialization value, reducing the accuracy of slope and offset values to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of contexts is increased to improve probability estimation accuracy, then coding efficiency is improved, but implementation complexity increases
Solution Approach 1:
The patent changes the parameter of context initialization from complex adaptive methods to simple fixed values. Each context is initialized with a fixed value between 0 and 255, which is then scaled to the probability range. This parameter change simplifies the system while maintaining coding efficiency by eliminating the need for complex training phases and adaptive adjustments.
2Device complexity
If the number of contexts is decreased to reduce implementation complexity, then device complexity is reduced, but probability estimation accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-defining fixed initialization values for each context before encoding begins. These values are carefully chosen to represent typical symbol statistics for different context types. This preliminary setup eliminates the need for runtime adaptation while providing sufficiently accurate probability estimates for effective entropy coding.
3Productivity
If a training phase is implemented to optimize slope and offset values, then coding efficiency is improved, but loss of time increases
Solution Approach 1:
The patent extracts and removes the training phase from the encoding process. Instead of performing adaptive optimization during runtime, the invention uses pre-determined fixed initialization values that have been optimized offline. This extraction eliminates the time-consuming training phase while preserving the benefits of optimized probability estimation through carefully selected fixed values.
Data Source
AI summary
A decoder includes an entropy decoder configured to derive a number of bins of the binarizations from the data stream using binary entropy decoding by selecting a context among different contexts and updating probability states associated with the different contexts, dependent on previously decoded portions of the data stream; a desymbolizer configured to debinarize the binarizations of the syntax elements to obtain integer values of the syntax elements; a reconstructor configured to reconstruct the video based on the integer values of the syntax elements using a quantization parameter, wherein the entropy decoder is configured to distinguish between 126 probability states and to initialize the probability states associated with the different contexts according to a linear equation of the quantization parameter, wherein the entropy decoder is configured to, for each of the different contexts, derive a slope and an offset of the linear equation from first and second four bit parts of a respective 8 bit initialization value.