HEVC Context Modeling for Lower-Memory Image Decoding
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Solution Overview
Problem
Existing image coding and decoding methods, particularly in High-Efficiency Video Coding (HEVC), suffer from increased memory usage due to the reliance on context models that utilize neighboring blocks, which are not adaptive to statistical variations and result in inefficient memory management.
Innovation Solution
An image coding and decoding method that determines contexts for control parameters based on first and second types, where the second type does not rely on the decoded control parameter of the upper block, allowing for reduced memory usage by adapting to a hierarchical tree structure unique to HEVC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If context models utilizing neighboring blocks are used in HEVC, then coding accuracy is improved, but memory usage increases
Solution Approach 1:
The patent segments the context model determination into two distinct types: Type 1 for control parameters where neighboring block information is beneficial, and Type 2 for control parameters where it is not required. This segmentation allows the system to apply memory-intensive context modeling only where it provides value, thereby reducing overall memory usage while maintaining coding accuracy for parameters that benefit from it.
Solution Approach 2:
The patent applies different context determination strategies to different control parameters based on their specific characteristics. For Type 1 control parameters, the system uses decoded control parameters from neighboring blocks to determine context. For Type 2 control parameters, it uses alternative methods that do not rely on neighboring block data. This local quality approach ensures optimal performance for each parameter type while minimizing unnecessary memory consumption.
2Manufacturing precision
If context models utilizing neighboring blocks are used, then coding precision is improved, but processing complexity increases
Solution Approach 1:
The patent divides the context modeling process into two distinct pathways (Type 1 and Type 2), allowing the system to bypass complex neighboring block analysis for control parameters where it is not needed. This segmentation reduces processing complexity by avoiding unnecessary computations while maintaining high coding precision for parameters that require detailed context analysis.
Solution Approach 2:
Instead of applying full context modeling with neighboring block analysis to all control parameters, the patent applies it partially only to Type 1 control parameters where it provides benefit. This partial action approach reduces processing complexity by eliminating excessive computations for Type 2 parameters while preserving coding precision where it matters most.
Data Source
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AI summary
The image decoding method includes: determining a context for use in a current block, from among a plurality of contexts (S204); and performing arithmetic decoding on a bit sequence corresponding to the current block, using the determined context (S210), wherein in the determining (S204): 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 (S206); 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 (S207).