LFNST Intra Prediction Mode Remapping for Chroma Coding
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, such as 4K and 8K ultra high definition, leads to higher transmission and storage costs due to increased data amounts, and there is a need for efficient compression techniques to handle immersive media formats like VR and AR, which require advanced image/video compression methods.
Innovation Solution
An image coding method and apparatus that derive an LFNST transform set using an intra mode for a luma block in a CCLM mode, updating the intra prediction mode for chroma blocks, remapping it when necessary, and using LFNST matrices to derive transform coefficients and residual samples, enhancing coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional transmission and storage methods are used for high-resolution images/videos, then image quality is maintained, but transmission cost and storage cost increase
Solution Approach 1:
The patent extracts and removes redundant information from high-resolution images/videos through advanced compression techniques. By identifying and eliminating duplicate or unnecessary data elements, the system maintains essential image quality while significantly reducing the data volume that requires transmission and storage, thereby lowering associated costs.
Solution Approach 2:
The patent transforms image/video data by changing its representation parameters through compression algorithms. By converting visual information into a more compact parameterized form that preserves perceptual quality, the system reduces the actual data size stored or transmitted, thus decreasing storage and transmission costs while maintaining acceptable image quality.
2Measurement precision
If high-resolution and high-quality images/videos are transmitted, then image quality is improved, but the transmitted information amount increases
Solution Approach 1:
The patent extracts only the essential visual information needed to maintain high perceived quality, removing redundant data. This selective extraction allows transmission of smaller data volumes while preserving the critical elements that define image quality, thus reducing transmitted information amount.
Solution Approach 2:
The patent changes the parameter representation of visual data from raw high-resolution formats to compressed parameterized formats. This transformation maintains the essential visual characteristics and quality attributes while dramatically reducing the quantity of data that must be transmitted.
3Quantity of substance
If advanced compression techniques are applied, then transmitted information amount is reduced, but coding complexity increases
Solution Approach 1:
The patent segments the compression process into distinct modular stages, each handling specific aspects of data reduction. By dividing the complex compression task into manageable segments with clear responsibilities, the system achieves effective data reduction while making the overall coding process more organized and manageable, thus controlling complexity.
Solution Approach 2:
The patent performs preliminary processing and preparation of data before applying the main compression algorithms. By pre-organizing and pre-processing the input data in advance, the system simplifies subsequent compression operations and reduces the computational complexity of the main coding stage, making the overall process more efficient.
Data Source
AI summary
An image decoding method according to the present document may comprise the steps of: deriving an intra prediction mode of a chroma block as a cross-component linear model (CCLM) mode on the basis of intra prediction mode information; updating the intra prediction mode of the chroma block on the basis of an intra prediction mode of a luma block corresponding to the chroma block; when the chroma block is not square, remapping the updated intra prediction mode to a wide-angle intra prediction mode; and determining an LFNST set including LFNST matrices on the basis of the remapped intra prediction mode.