LFNST Sub-partition Transform for Image Coding Efficiency
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, such as 4K and 8K UHD, leads to higher transmission and storage costs due to increased data amounts, and there is a need for efficient compression techniques to handle immersive media like VR and AR content.
Innovation Solution
The implementation of an image coding method using Low-Frequency Non-Separable Transform (LFNST) applied to sub-partition blocks, which derives modified transform coefficients based on an LFNST index and matrix, optimizing coding efficiency by zeroing out regions without transform coefficients and signaling the LFNST index accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality images/videos (4K, 8K UHD) are transmitted or stored, then image quality is improved, but transmission cost and storage cost increase due to increased data amount
Solution Approach 1:
The current block is divided into multiple sub-partition blocks, and LFNST is selectively applied to specific sub-partitions based on the presence of transform coefficients. This segmentation allows the transform to be applied only where necessary, improving compression efficiency without sacrificing image quality.
Solution Approach 2:
LFNST is applied locally to specific regions (sub-partitions) rather than uniformly across the entire block. The transform is selectively applied to sub-partitions containing transform coefficients, optimizing the balance between image quality and data reduction in different local areas.
2Productivity
If LFNST is applied to all sub-partition blocks, then compression efficiency is improved, but coding complexity increases
Solution Approach 1:
LFNST is applied partially rather than universally - only to sub-partitions that contain transform coefficients. This partial application improves compression efficiency for regions that need it while avoiding unnecessary processing in regions that don't benefit, thereby reducing overall coding complexity.
Solution Approach 2:
The application of LFNST is controlled by changing parameters (transform coefficient presence) in different sub-partitions. By dynamically adjusting whether LFNST is applied based on local coefficient distribution, the system optimizes compression efficiency while managing coding complexity through adaptive parameter control.
3Measurement precision
If LFNST index is parsed for every current block, then transform coding accuracy is improved, but signaling overhead increases
Solution Approach 1:
The LFNST index signaling is extracted and applied only to blocks that require it - specifically, blocks where transform coefficients are present in the second region. This selective extraction of the signaling mechanism reduces overall signaling overhead while maintaining transform coding accuracy where it is most beneficial.
Data Source
AI summary
An image decoding method according to the present document comprises a step of deriving a corrected transform coefficient, wherein the step of deriving the corrected transform coefficient comprises the steps of: determining whether the transform coefficient exists in a second region excluding the upper left first region of the current block; parsing a LFNST index on the basis of the determined result; and deriving the corrected transform coefficient on the basis of the LFNST index and a LFNST matrix, wherein the LFNST index can be parsed on the basis of the current block being divided into a plurality of sub-partition blocks, and the absence of the transform coefficient in some of the individual second regions for the plurality of sub-partition blocks.