LFNST Video Transform Coding for Sub-Partition Compression Efficiency
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images/videos, particularly in immersive media, leads to higher transmission and storage costs due to increased bit amounts, necessitating a more efficient image/video compression technique.
Innovation Solution
Implementing an image coding method that utilizes LFNST (Lifting Factorized Nested Transform) to determine and apply modified transform coefficients based on block tree-type and color format, allowing for enhanced coding efficiency, especially in sub-partition transform blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image coding methods are used for high-resolution images/videos, then image quality is maintained, but transmission cost and storage cost increase due to increased bit amount
Solution Approach 1:
The image block is divided into multiple sub-partition transform blocks, and LFNST is selectively applied to specific sub-partitions based on their characteristics. This segmentation allows the transform to be applied more precisely where needed, improving compression efficiency without sacrificing overall image quality.
Solution Approach 2:
LFNST is applied selectively to specific sub-partition transform blocks rather than uniformly across the entire block. The transform is applied based on local characteristics such as gradient direction and block size, allowing different processing strategies for different regions, thereby optimizing the balance between compression ratio and image quality.
2Productivity
If LFNST is applied to all transform blocks, then compression efficiency improves, but device complexity and computational load increase
Solution Approach 1:
LFNST is applied partially rather than universally - specifically to sub-partition transform blocks that meet certain criteria (size ≥ 4×4, specific gradient directions). This partial application achieves significant compression efficiency improvement while avoiding the excessive computational complexity that would result from applying LFNST to all transform blocks.
Solution Approach 2:
The transform block is segmented into multiple sub-partitions, and LFNST is applied selectively to specific sub-partitions based on their characteristics. This segmentation reduces the overall computational complexity by limiting LFNST application to only those regions where it provides the most benefit.
3Manufacturing precision
If transform is applied to small blocks, then coding precision improves, but transform index coding efficiency decreases
Solution Approach 1:
Different transform strategies are applied to different regions based on local block characteristics. For small sub-partition blocks, LFNST is applied selectively based on gradient direction and other local features, maintaining coding precision where needed while avoiding unnecessary transform index coding for blocks where it would not provide benefit.
Solution Approach 2:
The transform application decision is based on changing parameters such as block size, gradient direction, and sub-partition characteristics. By dynamically adjusting whether LFNST is applied based on these parameters, the system maintains coding precision for appropriate blocks while improving transform index coding efficiency overall.
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.