Adaptive Binarization for CABAC Speed-Efficiency Trade-Off
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Solution Overview
Problem
Arithmetic coding methods, such as CABAC, face inefficiencies in representing high-probability symbols due to finite precision constraints and adaptive estimation inefficiencies, leading to sub-optimal compression and decoding performance.
Innovation Solution
An adaptive binarization approach that adjusts the number of binary symbols based on the probability of the source, using a variable mapping unit value and size parameter to optimize encoding and decoding efficiency, ensuring accurate representation under finite precision constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If CABAC uses fixed precision registers for arithmetic coding, then the decoding speed improves, but the coding efficiency deteriorates for high-probability symbols
Solution Approach 1:
The patent applies dynamics by making the binarization strategy adaptive rather than fixed. The encoder dynamically selects between unary and truncated unary binarization methods based on the probability of the symbol being encoded. This dynamic adaptation allows the system to optimize for both speed and efficiency in different contexts, resolving the contradiction between fixed precision constraints and coding efficiency for high-probability symbols.
Solution Approach 2:
The patent changes the parameter of binarization method based on probability thresholds. When the probability exceeds a threshold, truncated unary binarization is used to improve speed; when probability is lower, standard unary binarization is used to maintain efficiency. This parameter change strategy allows the system to navigate the trade-off between decoding speed and coding efficiency.
2Adaptability or versatility
If CABAC adaptively estimates probability parameter p, then the coding adapts to non-stationary sources, but the number of bits increases due to estimation overhead
Solution Approach 1:
The patent applies local quality by making the binarization approach locally optimized for each symbol's probability characteristics. Instead of using a single global binarization method, the system locally adapts the binarization strategy based on the specific probability of each symbol, thereby achieving better overall coding efficiency while maintaining adaptability to non-stationary sources.
Solution Approach 2:
The patent uses partial adaptation by selectively applying truncated unary binarization only when probability thresholds are exceeded, rather than always using the most adaptive method. This partial application of adaptive techniques reduces the overhead cost while maintaining sufficient adaptability for non-stationary sources.
3Loss of information
If CABAC compresses high-probability symbols to fewer bits, then the compression ratio improves, but the processing speed at decoder deteriorates
Solution Approach 1:
The patent resolves this contradiction through dynamic binarization selection. The system dynamically adjusts the binarization method based on real-time probability estimates, allowing it to achieve high compression ratios for low-probability symbols while maintaining faster processing speeds for high-probability symbols through truncated unary binarization.
Solution Approach 2:
The patent changes the binarization parameter (unary vs. truncated unary) based on the probability parameter. This parameter change enables the system to optimize the balance between compression ratio and processing speed by selecting the appropriate binarization method for each symbol's probability characteristics.
Data Source
AI summary
The present invention first provides adaptive binarization in which a binarizer outputs binary symbol in length, which is variable adaptively to the probability of the source. When the probability is low, it is desirable to decrease the length of the binary symbols to improve the efficiency of arithmetic coding and reduce the complexity of coding calculation. On the other hand, when the probability is high, it is desirable to increase the length of the binary symbols to improve the overall process speed of a decoder. Specifically, a binarizer, according to the present invention, binarizes mapping unit values from a non-binary symbol into binary symbols. The number of binary symbols is inversely proportional to the size of the mapping unit value. In the present invention, the mapping unit value is made variable adaptively to the probability. Thus, the number of binary symbols from the binarizer is also variable adaptively to the probability parameter.