Transform-Based Image Coding with Adaptive LFNST Index Parsing
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, particularly in immersive media like VR and AR, leads to higher transmission and storage costs due to increased bit amounts, necessitating a more efficient image/video compression technique.
Innovation Solution
An image decoding method that derives modified transform coefficients by parsing an LFNST index based on whether an ISP is applied to a current block and the presence of significant coefficients in the DC component, utilizing individual transform skip flag values for color components, and applying dual tree chroma parsing when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality images/videos are transmitted or stored, then image quality and resolution are improved, but transmission cost and storage cost increase due to increased bit amount
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the transform type (primary transform, secondary transform, or transform skip) based on block characteristics such as prediction mode, block size, and gradient information. This adaptive transformation optimizes the compression ratio by selecting the most appropriate transform for each block, thereby reducing the bit amount required to represent high-quality images without compromising image quality
2Quantity of substance
If conventional transform coding is used for compression, then transmission cost is reduced, but coding efficiency is insufficient for high-resolution images
Solution Approach 1:
The patent segments the image into multiple blocks and applies different transform strategies to different blocks based on their local characteristics. By dividing the image into regions with similar properties and applying specialized transforms to each region, the coding efficiency is significantly improved compared to uniform conventional transform coding, achieving better compression for high-resolution images
Solution Approach 2:
The patent introduces dynamic adaptability by conditionally selecting among primary transform, secondary transform, and transform skip based on real-time analysis of block characteristics including prediction mode, block size, and gradient calculations. This dynamic selection mechanism optimizes coding efficiency by adapting the transform strategy to the specific content of each block, thereby improving overall compression performance for high-resolution images
3Productivity
If LFNST index coding is applied, then compression efficiency is improved, but complexity increases due to multiple parsing conditions
Solution Approach 1:
The patent performs preliminary determination of LFNST applicability by evaluating block characteristics (prediction mode, block size, gradient) before proceeding with full LFNST index coding. This preliminary action filters out blocks that do not benefit from LFNST, reducing the number of complex parsing operations needed while maintaining compression efficiency for suitable blocks
Data Source
AI summary
An image decoding method according to the present document comprises the step of deriving modified transform coefficients, wherein the step of deriving the modified transform coefficients comprises the step of parsing an LFNST index on the basis of a variable indicating whether an ISP is applied to a current block or whether an effective coefficient is present in a DC component of the current block, according to the tree type of the current block, wherein the variable may be derived on the basis of an individual transform skip flag value for a color component of the current block.