Arithmetic Coding Context Sharing for Variable Block Size Signals
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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 probability updates and decreased coding efficiency, especially when dealing with varying processing unit sizes and frequency components.
Innovation Solution
An image coding method that selects a shared context for signals with similar statistical properties across different processing unit sizes, reducing the number of contexts needed and increasing the accuracy of probability updates by using context sharing for signals with larger processing units and dedicated contexts for specific frequency components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate contexts are used for different processing unit sizes, then coding accuracy can be maintained for each size, but the number of contexts increases leading to memory size increase and reduced coding efficiency
Solution Approach 1:
The patent applies universality by creating a unified context structure where a single context can serve multiple processing unit sizes. The context is designed to be adaptable and reusable across different block sizes (e.g., 4x4, 8x8, 16x16), eliminating the need for separate dedicated contexts for each size while maintaining coding accuracy through selective application.
2Measurement precision
If more contexts are used to handle different signal characteristics, then coding precision improves, but the update frequency of each context decreases leading to inaccurate probability updates
Solution Approach 1:
The patent merges the functionality of multiple separate contexts into a single unified context that handles different processing unit sizes. This consolidation increases the update frequency for each context because updates are accumulated across more signal elements, leading to more accurate probability updates while maintaining coding precision through the unified structure.
3Measurement precision
If dedicated contexts are used for each processing unit size, then specific frequency components can be accurately coded, but the memory size for storing contexts increases
Solution Approach 1:
The patent creates a universal context structure that can be applied across different processing unit sizes and frequency components. This single context replaces multiple dedicated contexts, significantly reducing the memory size required for storing context information while maintaining the ability to accurately code specific frequency components through adaptive probability updates.
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.