Residual Coding Efficiency via Rice Parameter Binarization
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality images and videos, such as 4K or 8K Ultra High Definition, and the growth of virtual reality and augmented reality content require more efficient image/video compression techniques to reduce transmission and storage costs.
Innovation Solution
A method and device for enhancing image coding efficiency by performing binarization on residual information based on rice parameters, determining the decoding order of parity level flags, and limiting the number of significant coefficient flags and transform coefficient level flags to improve residual coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image/video compression techniques are used for high-resolution content, then transmission and storage costs increase, but image quality and resolution requirements cannot be met
Solution Approach 1:
The patent segments the transform coefficient level coding into multiple stages: significance flag coding, level value coding, and parity bit coding. This segmentation allows each component to be optimized independently, reducing the total bit rate while maintaining coding precision for high-resolution content.
Solution Approach 2:
The patent introduces a new dimension in coding by separating the level value representation into magnitude and parity components. This dimensional separation enables more efficient entropy coding by exploiting statistical dependencies between adjacent coefficients, thereby reducing the quantity of data needed to represent the same image quality.
2Productivity
If more residual information is coded to improve compression efficiency, then the complexity of the coding process increases
Solution Approach 1:
The patent performs preliminary binarization of residual information based on rice parameters before entropy coding. This preliminary action simplifies the subsequent coding process by pre-processing the data into a format that exploits statistical properties, thereby improving compression efficiency without proportionally increasing overall system complexity.
Solution Approach 2:
The patent changes the parameter representation by introducing adaptive rice parameters for binarization. These parameters are adjusted based on the local characteristics of the transform coefficients, allowing the coding process to adapt to different content types and achieve better compression efficiency while maintaining manageable complexity through parameter adaptation rather than structural complexity.
3Measurement precision
If the number of significant coefficient flags and transform coefficient level flags is increased to improve coding accuracy, then the amount of data to be decoded increases
Solution Approach 1:
The patent applies partial action by selectively coding only the necessary flags for significant coefficients rather than coding all possible flags. The significance flag and level value flag are coded only when needed, based on the actual presence and magnitude of transform coefficients, thereby maintaining coding accuracy for important features while reducing the overall data volume by avoiding redundant flag coding.
Data Source
AI summary
A method for decoding an image by a decoding device according to the present disclosure comprises the steps of: receiving a bit stream including residual information; deriving a quantized conversion factor of a current block on the basis of the residual information included in the bit stream; deriving a residual sample of the current block on the basis of the quantized conversion factor; and generating a reconstructed picture on the basis of the residual sample of the current block.