Arithmetic Context Sharing for Higher Image Coding Efficiency
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Solution Overview
Problem
Conventional arithmetic coding methods do not provide sufficient coding efficiency for image compression.
Innovation Solution
An image coding method that compresses images by obtaining current signals, generating binary signals through binarization, selecting contexts based on signal properties, performing arithmetic coding using coded probability information, and updating this information to enhance coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional arithmetic coding is used for image compression, then the coding process can be performed, but the coding efficiency is insufficient
Solution Approach 1:
The patent applies dynamics by making the context selection adaptive rather than static. The context for arithmetic coding is dynamically selected based on the block size of the current signal being coded. This allows the coding system to adapt to different image characteristics (different block sizes) and achieve better coding efficiency without losing compression performance
Solution Approach 2:
The patent changes the parameter of context selection based on block size. Instead of using a fixed context or a context independent of block size, the invention selects different contexts according to the block size parameter. This parameter change enables the arithmetic coding to better match the statistical properties of different block sizes, thereby improving coding efficiency
2Measurement precision
If multiple contexts are maintained for different block sizes, then coding accuracy can be improved, but the complexity of context management increases
Solution Approach 1:
The patent applies local quality by assigning different contexts to different block sizes. Each block size category (e.g., 4x4, 8x8, 16x16, 32x32) has its own dedicated context, allowing the probability information to be locally optimized for each block size type. This improves the accuracy of coded probability information while keeping the context management systematic and manageable through clear categorization
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.