Context Selection for Entropy Coding Transform Coefficients
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Solution Overview
Problem
Conventional context adaptive arithmetic coding in image coding techniques requires a significant load to select contexts for coding target coefficients, which can be inefficient.
Innovation Solution
An image coding method that selects contexts for arithmetic coding based on the position of sub-blocks within a coding-target block, using a context set corresponding to the sum of horizontal and vertical positions, thereby reducing the load and adapting to coefficient variations depending on frequency components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional context adaptive arithmetic coding is used to select contexts for coding target coefficients, then coding efficiency can be maintained, but a large amount of load is required to select contexts
Solution Approach 1:
The coding-target block is divided into multiple sub-blocks, and context selection is performed independently for each sub-block. This segmentation reduces the overall complexity by breaking down the large-scale context selection problem into smaller, more manageable units, thereby maintaining coding efficiency while reducing the computational load.
Solution Approach 2:
Different context sets are assigned to different sub-blocks based on their local characteristics (horizontal and vertical positions). This local quality approach allows the system to adapt to coefficient variations in different regions without requiring complex global context selection, thus reducing the overall load while preserving coding efficiency.
2Productivity
If context sets are switched based on coefficient variations, then coding efficiency is improved, but device complexity increases due to context set management
Solution Approach 1:
The patent assigns different context sets to different sub-blocks based on their local position characteristics. Each sub-block uses a context set that is optimized for its specific region, which improves coding efficiency by adapting to local coefficient variations without requiring complex dynamic context set management across the entire block.
Solution Approach 2:
Multiple context sets are merged into a unified selection mechanism based on sub-block position. Instead of managing separate context selection processes for each coefficient, the patent combines the context selection into a systematic approach where the sub-block position determines the context set, simplifying management while maintaining efficiency.
Data Source
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AI summary
An image coding method for coding an image on a block-by-block basis, includes: selecting, for each of a plurality of sub-blocks included in a coding-target block and each including a plurality of coefficients, a context for performing arithmetic coding on a parameter indicating a coding-target coefficient included in the sub-block from a context set corresponding to the sub-block, based on at least one reference coefficient located around the coding-target coefficient, the coding-target block being a transform unit (S301); and performing arithmetic coding on the parameter indicating the coding-target coefficient using probability information about the selected context, wherein, in the selecting, the context is selected from the context set, the context set corresponding to a sum of (i) a value indicating a position in a horizontal direction of the sub-block in the coding-target block and (ii) a value indicating a position in a vertical direction of the sub-block in the coding-target block (S302).