Image Decoding Context Determination for Memory Optimization
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Solution Overview
Problem
Existing image coding and decoding methods face challenges in reducing memory usage, particularly in High-Efficiency Video Coding (HEVC) where the context model using neighboring blocks increases memory usage.
Innovation Solution
An image decoding method using arithmetic decoding that determines a context for the current block by selecting between using decoded control parameters of neighboring blocks and not using them, depending on the signal type of the control parameter, thereby reducing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the context model uses decoded control parameters of neighboring blocks, then the coding precision is improved, but the memory usage increases
Solution Approach 1:
The patent applies local quality by differentiating the context model configuration based on signal type. For ref_idx-related parameters (list0, list1, lc), the context model does not use decoded control parameters of neighboring blocks, while for other parameters like split_coding_unit_flag and skip_flag, the context model does use neighboring block information. This selective approach optimizes memory usage while maintaining coding precision where beneficial.
2Reliability
If the context model uses decoded control parameters of neighboring blocks, then the image quality is improved, but the device complexity increases
Solution Approach 1:
The patent implements local quality by configuring different context model behaviors for different signal types. The context determination unit selectively uses decoded control parameters of neighboring blocks based on the specific parameter being coded, reducing unnecessary memory access and processing complexity while maintaining image quality for parameters where spatial correlation is beneficial.
3Loss of information
If arithmetic coding represents high probability events with fewer bits, then the loss of information is reduced, but the device complexity increases
Solution Approach 1:
The patent employs parameter changes by dynamically adjusting the context model parameters based on signal type. For ref_idx-related parameters, the context model uses a different configuration (not using neighboring block decoded parameters) compared to other parameters. This parameter adaptation optimizes the arithmetic coding efficiency by matching the statistical characteristics of different parameter types, reducing information loss while managing computational complexity.
Data Source
AI summary
The image decoding method includes: determining a context for use in a current block to be processed, from among a plurality of contexts; and performing arithmetic decoding on a bit sequence corresponding to the current block, using the determined context, wherein in the determining: the context is determined under a condition that control parameters of neighboring blocks of the current block are used, when the signal type is a first type, the neighboring blocks being a left block and an upper block of the current block; and the context is determined under a condition that the control parameter of the upper block is not used, when the signal type is a second type, and the second type is one of “ref_idx_l0” and “ref_idx_l1”.