Arithmetic Coding Context Segmentation for Frequency Coefficients
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Solution Overview
Problem
Arithmetic coding in video compression struggles with accurately determining contexts for image data, leading to decreased coding efficiency due to low frequency of updating symbol occurrence probabilities when contexts are classified in detail.
Innovation Solution
The method involves frequency-transforming image data, binarizing coefficients, and determining contexts common to multiple coefficients within segments to perform binary arithmetic coding and decoding, thereby increasing the frequency of updating probability information and improving coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contexts are classified in detail for arithmetic coding, then coding precision is improved, but the frequency of updating symbol occurrence probability decreases
Solution Approach 1:
The patent divides the unit of processing into multiple segments, and for each segment, determines one or more contexts common to multiple coefficients. This segmentation allows the system to update probability information more frequently by processing multiple coefficients within each segment using shared contexts, thereby resolving the contradiction between detailed context classification and updating frequency.
2Adaptability or versatility
If the number of contexts is increased for detailed classification, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple coefficients within a segment to share common contexts. Instead of assigning separate contexts to each coefficient, multiple coefficients are grouped and processed using shared probability information, reducing the total number of contexts required while maintaining adaptability to image data characteristics.
3Productivity
If contexts are classified in detail, then coding efficiency should be improved, but memory requirements increase
Solution Approach 1:
By merging multiple coefficients into segments that share common contexts, the patent reduces the total number of probability tables that need to be stored in memory. This approach maintains coding efficiency through adequate context classification while significantly reducing memory requirements by eliminating redundant probability information.
Data Source
AI summary
An image coding method includes: frequency-transforming image data to generate a unit of processing in a frequency domain; binarizing a coefficient included in the unit of processing, to generate a binary signal; determining, as a context for a binary signal, one or more contexts common to a plurality of the coefficients included in a segment which includes the binarized coefficient, the segment being one of a plurality of segments obtained by dividing the unit of processing; performing binary arithmetic coding on the binary signal according to probability information corresponding to the determined context; and updating, based on the binary signal, the probability information corresponding to the determined context.