Arithmetic Coding Context Sharing Across Image 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 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 accuracy of coded probability information by using context sharing based on processing unit size and frequency components.
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 updates and decreased prediction accuracy
Solution Approach 1:
The patent merges contexts by making the context index independent of processing unit size. Instead of having separate contexts for each block size (4x4, 8x8, 16x16, 32x32), the invention uses a unified context selection mechanism where the context index is determined by frequency component ranges and surrounding coefficient patterns, not by the processing unit size itself. This reduces the total number of contexts while maintaining adaptability through the unified context model.
Solution Approach 2:
The patent creates universal contexts that serve multiple processing unit sizes simultaneously. The same context is used across different block sizes (4x4, 8x8, 16x16, 32x32) for the same frequency component and surrounding pattern conditions. This universal context approach allows a single context to function across multiple processing scenarios, reducing the overall context count while preserving the ability to adapt to different block sizes through the context's dependence on frequency and surrounding patterns rather than explicit size dependency.
2Adaptability or versatility
If the number of contexts is large, then different processing conditions can be distinguished, but the update frequency of each context decreases leading to inaccurate probability predictions
Solution Approach 1:
The patent combines multiple context requirements into a smaller set of unified contexts. By removing the processing unit size dimension from context differentiation and relying instead on frequency component ranges and surrounding coefficient patterns, the invention consolidates what would have been numerous separate contexts into a manageable number of shared contexts. This merging increases the update frequency for each context since the same context serves multiple processing scenarios.
Solution Approach 2:
The patent changes the parameters used for context differentiation from including processing unit size to using only frequency component ranges and surrounding coefficient patterns. This parameter change reduces the total context count while maintaining the ability to distinguish different processing conditions through the remaining parameters (frequency ranges and surrounding patterns), thereby increasing update frequency without losing adaptability.
3Manufacturing precision
If separate contexts are used for different processing unit sizes, then coding can be optimized for each size, but the device complexity and memory requirements increase
Solution Approach 1:
The patent merges the context management systems for different processing unit sizes into a single unified context system. Instead of maintaining separate context tables or memory structures for each block size (4x4, 8x8, 16x16, 32x32), the invention uses a single context selection mechanism that works across all sizes. This reduces device complexity and memory requirements while preserving coding optimization through the unified context's dependence on frequency and surrounding patterns.
Solution Approach 2:
The patent creates universal contexts that serve multiple processing unit sizes simultaneously, eliminating the need for size-specific context storage. The same context structure and memory are reused across different block sizes, reducing overall device complexity and memory footprint while maintaining the ability to optimize coding for each size through the context's adaptive selection based on frequency and surrounding patterns.
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.