Transform-Based Image Coding With Context-Aware LFNST Index Coding
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, particularly in immersive media formats like VR and AR, has led to higher transmission and storage costs due to increased bit amounts, necessitating a more efficient image/video compression technique.
Innovation Solution
Implementing a method and apparatus that utilize LFNST and MTS transforms to enhance image coding efficiency, including deriving residual samples and generating reconstruction pictures based on context-dependent context information for LFNST indexes, and encoding/decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image coding techniques are used for high-resolution images/videos, then image quality is maintained, but transmission cost and storage cost increase due to increased bit amount
Solution Approach 1:
The patent applies Low Frequency Non-Separable Transform (LFNST) and Multiple Transform Selection (MTS) to change the transformation parameters from conventional separable transforms. This transforms the residual signal in a different manner, achieving better energy compaction and compression efficiency while maintaining image quality, thus reducing bit amount for high-resolution images
2Loss of energy
If LFNST is applied to improve compression efficiency, then transmission cost is reduced, but coding complexity increases due to additional transform operations
Solution Approach 1:
The patent implements LFNST selectively rather than universally. The transform is applied only to specific blocks based on criteria such as block size, prediction mode, or residual characteristics. This partial application reduces the overall coding complexity while still achieving compression efficiency improvements for suitable blocks, thereby reducing transmission cost without excessive complexity increase
3Quantity of substance
If multiple transform types are used to enhance compression performance, then bit amount is reduced, but decoding complexity increases
Solution Approach 1:
The patent performs Multiple Transform Selection (MTS) during the encoding phase, where the optimal transform type is selected and its index is signaled in the bitstream. During decoding, the transform type is determined by reading this pre-signalized index, avoiding the need for complex real-time transform selection at the decoder. This preliminary action at the encoder reduces decoding complexity while maintaining the bit reduction benefits of multiple transform types
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.