Transform-Based Image Coding with Context-Adaptive LFNST Indexing
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, including immersive media, leads to higher transmission and storage costs due to increased bit amounts, necessitating a more efficient image/video compression technique.
Innovation Solution
The method involves deriving residual samples through LFNST or MTS and generating a reconstruction picture based on an LFNST transform set, with context-dependent bin derivation for LFNST index coding to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image coding techniques are used for high-resolution images, then image quality is maintained, but transmission and storage costs increase due to increased bit amounts
Solution Approach 1:
The image block is divided into multiple sub-blocks for separate transform processing. The transform unit is segmented into first and second sub-blocks, allowing different transform kernels to be applied to different regions, improving compression efficiency while maintaining image quality
Solution Approach 2:
The transform kernel selection is made dynamic based on the characteristics of each sub-block. Different transform kernels are selected for different sub-blocks according to their local image characteristics, optimizing the balance between compression ratio and reconstruction quality
2Quantity of substance
If LFNST is applied to improve compression efficiency, then bit amount is reduced, but coding complexity increases due to additional transform steps
Solution Approach 1:
LFNST is applied selectively to specific sub-blocks based on local image characteristics rather than uniformly to the entire block. This localized application reduces the overall complexity while maintaining compression efficiency where it is most beneficial
Solution Approach 2:
Instead of applying LFNST to the entire transform unit, the method applies it partially to only certain sub-blocks where it provides the most benefit. This partial action approach reduces complexity while achieving significant compression improvements
3Productivity
If context-dependent bin derivation is used for LFNST index coding, then coding efficiency is improved, but processing complexity increases
Solution Approach 1:
Context models are prepared and configured in advance for different bin positions and block types. This preliminary setup allows the decoding process to efficiently select and use appropriate context models without complex real-time calculations, improving coding efficiency while controlling processing complexity
Data Source
AI summary
An image decoding method according to the present document may comprise the steps of: deriving residual samples by applying at least one of LFNST and MTS to transform coefficients; and generating a reconstructed picture on the basis of the residual samples, wherein the LFNST is performed on the basis of an LFNST transform set, an LFNST kernel included in the LFNST transform set, and an LFNST index indicating the LFNST kernel, a first bin of a syntax element bin string for the LFNST index is derived on the basis of different context information according to a tree type of a current block, and a second bin of the syntax element bin string is derived on the basis of preconfigured context information.