Arithmetic Decoding of Last Position Bits With Segmented Contexts
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Solution Overview
Problem
Conventional context adaptive binary arithmetic coding and decoding techniques face challenges in accurately predicting symbol probabilities, leading to decreased coding efficiency due to inappropriate switching among contexts, especially when different bit positions have varying probabilities of symbol occurrence.
Innovation Solution
The method involves binarizing last position information to generate binary signals with varying lengths, allowing for context-switched arithmetic coding of prefix parts and fixed probability decoding of suffix parts, with the last bit position using an exclusive context to enhance coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If context adaptive binary arithmetic coding is used for last position information, then coding efficiency can be improved through probability prediction, but inappropriate context switching decreases accuracy and coding efficiency
Solution Approach 1:
The patent segments the last position information into multiple bins based on block size (e.g., 4 bins for 4x4 blocks, 9 bins for 8x8 blocks, 16 bins for 16x16 blocks). Each bin corresponds to a specific range of last position values and uses a dedicated context model. This segmentation allows the coding system to adapt to the varying probability distributions at different position ranges without requiring complex dynamic context switching, thereby resolving the contradiction between coding efficiency and prediction accuracy.
2Productivity
If multiple contexts are used for different bit positions, then coding efficiency improves, but device complexity and memory requirements increase
Solution Approach 1:
The patent applies local quality by assigning different context models to different bins based on their local characteristics (block size and position range). Each bin uses a context model optimized for its specific local probability distribution rather than using a single global context model. This approach improves coding efficiency for each local region while avoiding the complexity of dynamic context switching by making the context selection static and deterministic based on bin assignment.
Data Source
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AI summary
An image coding method including: binarizing (S401) last position information to generate (i) a binary signal which includes a first signal having a length smaller than or equal to a predetermined maximum length and does not include a second signal or (ii) a binary signal which includes the first signal having the predetermined maximum length and the second signal; first coding (S402) for arithmetically coding each of binary symbols included in the first signal using a context switched among a plurality of contexts according to a bit position of the binary symbol; and second coding (S404) for arithmetically coding the second signal using a fixed probability when the binary signal includes the second signal, wherein in the first coding, a binary symbol at a last bit position of the first signal is arithmetically coded using a context exclusive to the last bit position, when the first signal has the predetermined maximum length.