Transform-Based Image Coding With Adaptive LFNST Parsing
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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
The method involves deriving modified transform coefficients based on transform skip flags for each color component, parsing an LFNST index, and applying an LFNST kernel to enhance coding efficiency, particularly in dual tree luma and chroma components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement 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 image block is divided into multiple sub-blocks, and different transform processing methods are applied to different sub-blocks based on their local characteristics. This segmentation allows for more precise representation of image details while reducing redundant information, thereby improving compression efficiency without sacrificing image quality
Solution Approach 2:
The transform processing method is dynamically selected based on the characteristics of each sub-block. The decoding apparatus determines whether to apply transform skip mode or LFNST to each sub-block based on sub-block transform skip flags, enabling adaptive processing that optimizes the balance between compression ratio and image quality
2Manufacturing precision
If transform processing is applied to all sub-blocks, then coding precision is improved, but device complexity increases
Solution Approach 1:
Different transform processing strategies are applied to different sub-blocks based on their local characteristics. Sub-blocks with significant residual energy undergo LFNST processing, while others use transform skip mode. This local adaptation improves coding precision where needed while reducing overall processing complexity
Solution Approach 2:
Instead of applying complex transform processing to all sub-blocks, the method selectively applies LFNST only to sub-blocks that benefit from it, determined by the sub-block transform skip flags. This partial action approach achieves sufficient coding precision while avoiding unnecessary processing complexity
Data Source
AI summary
An image decoding method according to the present document comprises the steps of: receiving residual information in a bitstream, wherein the residual information includes a transform skip flag for each individual component of the current block; deriving corrected transform coefficients, wherein the step for deriving includes a step for setting a variable indicating whether effective coefficients exist at positions other than that of a DC component of the current block to 0 when even one of the values of the transform skip flags for the individual components is 0, and a step for parsing an LFNST index on the basis of the variable being 0; and deriving an LFNST kernel for applying LFNST on the basis of the LFNST index, wherein the variable being 0 may indicate that effective coefficients exist at positions other than that of the DC component.