Arithmetic Coding for Sample Adaptive Offset Processing
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Solution Overview
Problem
Existing image coding methods using arithmetic coding suffer from poor processing efficiency, leading to difficulties in suppressing processing delay during coding.
Innovation Solution
The proposed image coding method employs context arithmetic coding to consecutively code information related to sample adaptive offset (SAO) processing for image regions, using variable probability arithmetic coding. Additionally, bypass arithmetic coding is used to code other SAO processing information with a fixed probability, thereby improving processing efficiency by reducing frequent switching between coding types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If context arithmetic coding is used to code SAO processing information with variable probability, then coding efficiency is improved, but processing delay increases due to frequent switching between coding types
Solution Approach 1:
The patent segments the arithmetic coding process into two distinct modes: context arithmetic coding for probability-based information and bypass arithmetic coding for fixed-probability information. This segmentation allows each mode to be optimized independently, reducing the overhead of frequent mode switching while maintaining high coding efficiency for both types of data.
Solution Approach 2:
The patent dynamically selects between context and bypass arithmetic coding modes based on the specific characteristics of the data being encoded. By adaptively choosing the appropriate coding mode for each piece of information, the system maximizes coding efficiency while minimizing processing delay through intelligent mode selection rather than frequent switching.
2Adaptability or versatility
If frequent switching between context and bypass arithmetic coding is performed, then coding flexibility is improved, but processing efficiency deteriorates
Solution Approach 1:
The patent applies different coding qualities to different types of information: context arithmetic coding is used for information requiring adaptive probability modeling, while bypass arithmetic coding is used for information with fixed probability characteristics. This local quality approach ensures each type of information is processed with the most appropriate method, maintaining coding flexibility without sacrificing processing efficiency.
Solution Approach 2:
The system dynamically adjusts the coding approach based on the local characteristics of the data being encoded. By making real-time decisions about which coding mode to use for each piece of information, the system maintains high adaptability while improving processing efficiency through reduced mode-switching overhead.
Data Source
AI summary
An image coding method includes: performing context arithmetic coding to consecutively code (i) first information indicating whether or not to perform sample adaptive offset (SAO) processing for a first region of an image and (ii) second information indicating whether or not to use, in the SAO processing for the first region, information on SAO processing for a region other than the first region, the context arithmetic coding being arithmetic coding using a variable probability, the SAO processing being offset processing on a pixel value; and performing bypass arithmetic coding to code other information which is information on the SAO processing for the first region and different from the first information or the second information, after the first information and the second information are coded, the bypass arithmetic coding being arithmetic coding using a fixed probability.