Entropy Decoder Context Initialization With 4-Bit Probability Parameters
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Solution Overview
Problem
Existing video coding technologies face challenges in achieving a balance between coding efficiency and implementation complexity, particularly in context-adaptive binary entropy coding, where the accuracy of slope and offset values for probability estimation initialization can lead to suboptimal results and increased memory demands.
Innovation Solution
The proposed solution involves reducing the accuracy of slope and offset values for initializing probability estimation in context-adaptive binary entropy coding, allowing for a compromise between coding efficiency and complexity, and implementing a decoder and encoder configuration that distinguishes between 126 probability states, with each context using a linear equation initialized by deriving slope and offset from specific bits of an 8-bit value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high accuracy slope and offset values are used for probability estimation initialization, then coding efficiency is improved, but memory demands and implementation complexity increase
Solution Approach 1:
The patent changes the precision parameter of slope and offset values from high accuracy (e.g., 8-bit or higher) to reduced accuracy (e.g., 4-bit or lower). This parameter change directly reduces memory storage requirements and simplifies the data structures needed to hold probability estimation parameters, while the patent demonstrates that the reduced precision still maintains adequate coding performance for video compression applications.
2Measurement precision
If more contexts are provided for probability estimation, then coding accuracy is improved, but adaptation fails due to low symbol frequency in individual contexts
Solution Approach 1:
The patent applies partial action by using reduced precision slope and offset values rather than full precision values. This partial precision approach provides sufficient probability estimation accuracy for most coding scenarios without requiring the full computational and memory resources that would be needed for high-precision estimation across all possible contexts, thereby avoiding the adaptation failure that occurs when too many contexts are used.
3Adaptability or versatility
If quantization parameter dependent initialization is used, then probability estimation is adapted to different video contents, but implementation complexity increases
Solution Approach 1:
The patent changes the initialization approach from complex, adaptive probability estimation to a simpler quantization parameter dependent initialization. By using the quantization parameter (which is already available in the video coding process) to directly initialize the reduced-precision slope and offset values, the patent achieves adaptability to different video contents and quantization settings without requiring complex training phases or sophisticated adaptation algorithms.
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.