LFNST Transform Coding for Sub-Partition Image Compression
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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, necessitates a more efficient image/video compression technique to reduce transmission and storage costs.
Innovation Solution
The implementation of an image coding method using Localized Flexible Non-Separable Transform (LFNST) applied to sub-partition transform blocks, which involves deriving prediction samples based on intra prediction mode information, selecting an LFNST matrix, and applying it to derive transform coefficients for improved compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality images/videos are transmitted or stored using conventional methods, then image quality is maintained, but transmission cost and storage cost increase
Solution Approach 1:
The current block is divided into multiple sub-partition transform blocks, allowing independent transform processing for each sub-block. This segmentation enables more efficient compression by adapting the transform to local characteristics, reducing the overall bitrate required to maintain image quality
Solution Approach 2:
Different LFNST matrices are selected for different sub-partition transform blocks based on their specific characteristics (intra prediction mode, block size). This local adaptation optimizes compression efficiency for each region while maintaining overall image quality, reducing total transmission and storage requirements
2Productivity
If conventional transform methods are used for image coding, then implementation is simple, but compression efficiency is insufficient for high-resolution images
Solution Approach 1:
The patent introduces LFNST index parameters to select from multiple transform matrices based on intra prediction mode and block characteristics. This parameter-based approach enables adaptive compression efficiency improvement while keeping the implementation structure manageable through standardized matrix selections
3Productivity
If LFNST is applied to improve transform index coding efficiency, then compression performance increases, but processing complexity increases
Solution Approach 1:
LFNST matrices are pre-defined and pre-selected based on intra prediction modes before actual transform processing. This preliminary preparation reduces runtime complexity by avoiding real-time matrix selection, enabling improved compression efficiency with manageable processing overhead
Data Source
AI summary
An image decoding method according to the present document may comprise the steps of: if a current block is partitioned into sub-partition transform blocks, deriving a prediction sample of the current block on the basis of intra prediction mode information; determining an LFNST set including LFNST matrices on the basis of an intra prediction mode derived from the intra prediction mode information; selecting one of the LFNST matrices on the basis of the LFNST set and the LFNST index; deriving transform coefficients for the sub-partition transform blocks on the basis of the selected LFNST matrix; and deriving residual samples for the current block on the basis of the transform coefficients.