Residual Coding Binarization Using a Limited Rice Parameter
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Solution Overview
Problem
The increasing demand for high resolution and high quality images/videos, such as 4K or 8K Ultra High Definition, poses challenges in efficient compression, transmission, and storage due to the higher amount of information required, especially in applications like virtual reality, artificial reality, and immersive media.
Innovation Solution
A method and device for enhancing image coding efficiency by performing a binarization process on residual information based on a rice parameter, with the maximum value of the rice parameter set as 3, to optimize residual coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution and high quality image/video (4K or 8K UHD) are transmitted or stored, then image quality is improved, but transmission and storage costs are increased
Solution Approach 1:
The patent applies parameter changes by modifying the Rice parameter (limiting it to maximum value of 3) and adjusting binarization thresholds in the entropy coding process. These parameter adjustments optimize the compression efficiency for high resolution images, reducing the amount of data needed to represent the same visual information while maintaining 4K or 8K quality standards
2Productivity
If residual coding efficiency is improved by binarization process, then compression efficiency is improved, but computational complexity is increased
Solution Approach 1:
The patent simplifies the binarization process by constraining the Rice parameter to a maximum value of 3 and using fixed threshold values (0, 1, 2, 3) for coefficient magnitude classification. This parameter constraint reduces the computational complexity of determining binarization modes while maintaining compression efficiency through optimized entropy coding of transform coefficients
Data Source
AI summary
A method for decoding a picture performed by a decoding apparatus according to the present disclosure includes receiving a bitstream including residual information, deriving a quantized transform coefficient for a current block based on the residual information included in the bitstream, deriving a transform coefficient from the quantized transform coefficient based on a dequantization process, deriving a residual sample for the current block by applying an inverse transform to the derived transform coefficient, and generating a reconstructed picture based on the residual sample for the current block.