LFNST-Based Image Coding for Per-Component Compression Efficiency
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, particularly in immersive media formats, necessitates a more efficient image/video compression technique to reduce transmission and storage costs.
Innovation Solution
An image coding method and apparatus utilizing LFNST (Lifting Factorization Based Non-Separable Transform) that adapts to different tree types for chroma and luma components, with flag variables determining the application of LFNST for each color component.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality image/video data is transmitted or stored using conventional methods, then image quality is maintained, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies Low Frequency Non-Separable Transform (LFNST) as a parameter change technique to transform residual coefficients in the frequency domain. This transform changes the representation parameters of the image data, enabling more efficient compression while maintaining high-quality reconstruction, thus reducing transmission and storage costs without sacrificing image quality
2Productivity
If LFNST is applied to all color components, then compression efficiency improves, but computational complexity and processing overhead increase
Solution Approach 1:
The patent implements selective LFNST application based on local characteristics of different color components. The transform is conditionally applied to luma and chroma components depending on their specific properties and the predicted syntax elements, rather than uniformly applying it to all components. This localized approach optimizes compression efficiency for components that benefit most while reducing unnecessary computational overhead
Solution Approach 2:
The patent uses partial action by selectively applying LFNST only to certain color components (luma and/or chroma) based on predicted syntax elements and block characteristics. Instead of applying the transform excessively to all components, the method applies it partially where needed, balancing compression efficiency with computational complexity
Data Source
AI summary
An image decoding method according to the present document may comprise the steps of: receiving image information including residual information and an LFNST index from a bitstream; deriving a transform coefficient on the basis of the residual information; deriving a flag variable related to whether LFNST is applied to a current block on the basis of the LFNST index; and performing the LFNST on the basis of the flag variable and the transform coefficient, wherein the flag variable is derived for each color component of the current block.