Image Coding With LFNST Scaling for Efficient Chroma Compression
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, including immersive media, necessitates a highly efficient image/video compression technique to reduce transmission and storage costs.
Innovation Solution
An image decoding method that applies an LFNST to transform coefficients, determining whether a scaling list is applied based on the tree type and flag information, and adjusts quantization accordingly for luma and chroma components, enhancing compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality images/videos are transmitted or stored, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies different scaling list parameters and quantization strategies based on block size, tree type (single-tree vs. dual-tree), and component type (luma vs. chroma). By dynamically adjusting quantization parameters according to these conditions, the system achieves efficient compression while maintaining high image quality, thus reducing transmission and storage costs without sacrificing quality.
2Quantity of substance
If conventional compression techniques are used, then transmission cost is reduced, but image quality deteriorates
Solution Approach 1:
The patent applies different quantization and scaling strategies to different regions and components of the image. Specifically, it applies scaling lists selectively based on block size and tree type, and applies different quantization parameters to luma and chroma components. This localized approach maintains high image quality in critical regions while achieving efficient compression overall.
3Productivity
If LFNST is applied to chroma component, then quantization efficiency is improved, but complexity increases
Solution Approach 1:
The patent applies LFNST selectively rather than universally. It applies LFNST to chroma components in single-tree type blocks where it provides significant quantization efficiency improvement, while avoiding its application in dual-tree type blocks or other cases where the benefit is minimal. This partial application approach achieves good quantization efficiency while controlling coding complexity.
Data Source
AI summary
An image decoding method according to the present document comprises the steps of: applying an LFNST to transform coefficients so as to derive modified transform coefficients; and deriving residual samples for a target block on the basis of an inverse primary transform for the modified transform coefficients, wherein the step of deriving the transform coefficients comprises: determining whether a scaling list is applied to a current block on the basis of a tree type of the current block and whether the LFNST is applied; and deriving transform coefficients for the current block from residual information on the basis of the determination result, and when the tree type of the current block is a single tree and a chroma component, the scaling list can be applied.