Entropy Context Initialization for Low-Memory Video Coding
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Solution Overview
Problem
Current context-adaptive binary entropy coding methods face challenges in achieving a balance between coding efficiency and implementation complexity, particularly in accurately initializing probability states for video coding, which can lead to suboptimal compression rates and increased memory demands.
Innovation Solution
The proposed solution involves a decoder and encoder configuration that distinguishes between 126 probability states, initializing these states using a linear equation with derived slope and offset values from 8-bit initialization values, and updating probability states based on previously decoded or encoded portions of the data stream.
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 device complexity and memory requirements increase
Solution Approach 1:
The patent changes the parameter of probability state initialization from complex context-specific values to a unified linear equation based on the quantization parameter. This reduces the number of contexts from many context-specific probability estimations to a single linear model, thereby reducing device complexity while maintaining coding efficiency through the universality of the linear initialization approach across all contexts.
2Measurement precision
If high precision slope and offset values are used for probability state initialization, then coding accuracy is improved, but memory requirements and device complexity increase
Solution Approach 1:
The patent changes the precision parameter of slope and offset values from high-precision representations to reduced-precision values that are still sufficient for accurate probability state initialization. This parameter change reduces the memory quantity required to store these values while maintaining the essential accuracy needed for effective entropy coding, resolving the contradiction between precision and memory requirements.
3Productivity
If extensive training phases are performed to initialize probability estimations, then coding efficiency is improved, but loss of time and processing overhead increase
Solution Approach 1:
The patent performs preliminary action by establishing a linear equation-based initialization method that can be directly applied without extensive training phases. The slope and offset values are predetermined and can be initialized immediately, eliminating the need for time-consuming training processes while still achieving effective probability estimation. This preliminary setup enables fast initialization and reduces processing overhead.
4Measurement precision
If context-specific probability estimations are used for different syntax elements, then coding accuracy is improved, but device complexity and initialization complexity increase
Solution Approach 1:
The patent applies universality by creating a single linear equation-based initialization method that serves all contexts and syntax elements. Instead of designing separate context-specific initialization procedures, the universal linear model using quantization parameter, slope, and offset can be applied uniformly across all contexts, thereby reducing initialization complexity while maintaining the ability to accurately represent different symbol statistics through the linear relationship.
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.