LFNST Transform Indexing for Sub-Partition Video Blocks
Find Innovative SolutionsGenerate Solutions
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 coding method and apparatus utilizing LFNST (Lifting Factorized Nested Transform) for transform index coding, applied to sub-partition transform blocks, determining applicability based on block height and width, and parsing LFNST indices accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional compression techniques are used for high-resolution images/videos, then transmission and storage costs increase, but image quality and resolution requirements cannot be met
Solution Approach 1:
The image block is divided into multiple sub-partition blocks, and LFNST is selectively applied to specific sub-partitions based on their characteristics. This segmentation allows different compression strategies to be applied to different regions, achieving better overall compression efficiency while maintaining image quality.
Solution Approach 2:
The patent applies LFNST selectively to specific sub-partition blocks based on their local characteristics (such as gradient magnitude). This local quality approach ensures that regions requiring higher fidelity are processed with more sophisticated transforms, while other regions use simpler methods, optimizing the balance between quality and compression.
2Productivity
If LFNST is applied to all blocks, then compression efficiency improves, but computational complexity and processing overhead increase
Solution Approach 1:
Instead of applying LFNST to all blocks, the patent applies it only to selected sub-partition blocks that meet specific criteria (such as having sufficient gradient magnitude). This partial action approach achieves most of the compression benefit while significantly reducing computational complexity compared to universal application.
Solution Approach 2:
The patent introduces conditional parameters (block size, gradient magnitude thresholds, sub-partition configuration) to control LFNST application. By dynamically changing these parameters based on content characteristics, the system optimizes the balance between compression efficiency and processing complexity.
3Speed
If transform indexing is simplified, then coding speed increases, but transform precision and compression performance decrease
Solution Approach 1:
The transform indexing process is segmented into multiple stages: gradient calculation, threshold evaluation, and selective LFNST application. This segmentation allows the system to quickly eliminate blocks that don't meet criteria while applying precise transforms only where needed, maintaining both speed and precision.
Solution Approach 2:
The patent performs preliminary gradient calculation and threshold evaluation before applying LFNST. This preliminary action identifies candidate blocks in advance, allowing the main transform operation to be applied only to selected blocks, thus maintaining coding speed while ensuring transform precision where applicable.
Data Source
AI summary
A video decoding method according to the present document comprises a step of deriving a modified transform coefficient, and the step of deriving the transform coefficient may comprise the steps of: determining whether an LFNST can be applied to the height and width of a current block on the basis of the tree type and color format of the current block; parsing an LFNST index if the LFNST can be applied; and deriving the modified transform coefficient on the basis of the LFNST index and an LFNST matrix.