Shared Context Arithmetic Coding Across Variable Block Sizes
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Solution Overview
Problem
Conventional arithmetic coding methods for video data compression do not provide sufficient coding efficiency due to the large number of contexts required, leading to inaccurate updates and decreased prediction accuracy of probability information.
Innovation Solution
An image coding method that selects a shared context for signals with similar statistical properties across different processing units, reducing the number of contexts needed and increasing the accuracy of probability updates, thereby enhancing coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional arithmetic coding uses separate contexts for each processing unit size, then coding can be adapted to different block sizes, but the number of contexts becomes large leading to inaccurate probability updates and decreased coding efficiency
Solution Approach 1:
The patent merges contexts by making the context index independent of processing unit size. Instead of having separate context tables for each block size (4×4, 8×8, 16×16, 32×32), the invention uses a unified context selection mechanism where the same context index can be applied across different processing unit sizes, thereby reducing the total number of contexts while maintaining adaptability.
Solution Approach 2:
The patent makes contexts universal by designing them to function across multiple processing unit sizes. The context selection is based on frequency component regions and surrounding conditions rather than being tied to specific block sizes, allowing the same context to serve multiple processing unit sizes and improving coding efficiency through more accurate probability updates.
2Measurement precision
If the number of contexts is increased to cover all processing unit sizes and conditions, then coding accuracy for specific cases improves, but the complexity of context management and memory requirements increase
Solution Approach 1:
The patent applies local quality by selecting contexts based on specific local characteristics such as frequency component regions (low-frequency vs. high-frequency) and surrounding conditions (presence of non-zero coefficients). This allows the system to maintain high prediction accuracy for specific local cases without requiring separate contexts for all possible processing unit sizes and conditions.
3Reliability
If separate contexts are maintained for different processing unit sizes, then each context can be optimized for its specific size, but memory usage and computational overhead increase
Solution Approach 1:
The patent merges context storage by eliminating redundant context tables for different processing unit sizes. Instead of maintaining separate context data structures for each block size, the invention uses a single unified context selection mechanism that works across all processing unit sizes, thereby reducing memory usage while maintaining coding efficiency through appropriate context selection based on frequency regions and surrounding conditions.
Data Source
AI summary
An image coding method comprising: obtaining current signals to be coded of each of the processing units of the image; generating a binary signal by performing binarization on each of the current signals to be coded; selecting a context for each of the current signals to be coded from among a plurality of contexts; performing arithmetic coding of the binary signal by using coded probability information associated with the context selected in the selecting; and updating the coded probability information based on the binary signal, wherein, in the selecting, the context for the current signal to be coded is selected, as a shared context, for a signal which is included in one of a plurality of processing units and has a size different from a size of the processing unit including the current signal to be coded.