Shared Context Arithmetic Coding for Multi-Size Image Blocks
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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 symbol occurrence probabilities.
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 frequency of updates, thereby enhancing prediction accuracy and 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 it can adapt 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 across different processing unit sizes by introducing a context sharing mechanism. When a processing unit of a first size is divided into multiple processing units of a second size, the context for the first processing unit is shared with the divided processing units. This reduces the total number of contexts from being proportional to the number of processing unit sizes to a single shared context, thereby improving the accuracy of probability updates while maintaining adaptability to different block sizes.
2Adaptability or versatility
If the number of contexts is increased to cover all processing unit sizes, then coding can be adapted to different block sizes, but the update frequency of each context decreases leading to lower prediction accuracy
Solution Approach 1:
The patent implements a universal context that serves multiple processing unit sizes simultaneously. The context is designed to be multi-functional, handling both the first processing unit size and the second processing unit size (after division) without requiring separate dedicated contexts. This universal approach increases the update frequency for each context while maintaining support for multiple block sizes, thereby improving coding efficiency.
3Manufacturing precision
If separate contexts are used for different processing unit sizes, then each context can be optimized for its specific size, but the overall device complexity increases
Solution Approach 1:
The patent combines multiple size-specific contexts into a single shared context through the context sharing mechanism. Instead of maintaining separate contexts for each processing unit size, the system uses one context that is shared across different sizes, significantly reducing the complexity of context management while still providing optimized coding for specific sizes through the division relationship.
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.