LFNST Image Coding Method for High-Resolution Compression
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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 bit rates, resulting in increased transmission and storage costs. Additionally, the need for efficient compression techniques is exacerbated by the rise of immersive media like virtual and augmented reality.
Innovation Solution
The proposed solution involves an image coding method and apparatus that utilizes Low-Frequency Non-Separable Transform (LFNST) to enhance coding efficiency. This method includes deriving the position of the last significant coefficient in a current block, determining the existence of significant coefficients in specific regions, and applying an LFNST matrix to modify transform coefficients, thereby reducing the amount of data required for compression.
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 due to higher bit rates
Solution Approach 1:
The patent applies Low-Frequency Non-Separable Transform (LFNST) to transform coefficient blocks, changing the transformation parameters from conventional separable transforms to non-separable transforms. This parameter change enables more efficient energy compaction in the transform domain, allowing high-quality image reconstruction at lower bit rates, thereby reducing transmission and storage costs while maintaining image quality
2Productivity
If conventional transform methods are used for image coding, then implementation is simple, but coding efficiency is insufficient for high-resolution images
Solution Approach 1:
The patent segments the transform coefficient block into multiple sub-blocks and applies LFNST selectively to specific sub-blocks based on the presence of significant coefficients. This segmentation approach enables efficient coding by applying complex transforms only where necessary, improving coding efficiency while controlling implementation complexity through conditional application
Solution Approach 2:
The patent performs preliminary analysis to identify the position of the last significant coefficient before applying LFNST. This preliminary action allows the encoder to determine which sub-blocks require LFNST application, optimizing the transform process in advance and improving overall coding efficiency without excessive complexity
3Productivity
If LFNST is applied to all sub-blocks, then compression efficiency increases, but bit rate increases due to additional LFNST index information
Solution Approach 1:
The patent applies LFNST selectively to specific sub-blocks rather than uniformly to the entire coefficient block. By determining the position of the last significant coefficient and applying LFNST only to relevant sub-blocks, the patent achieves local optimization of compression efficiency while minimizing the additional bit rate required for LFNST index information
4Measurement precision
If transform coefficients are densely populated, then image detail is preserved, but data amount increases requiring more compression
Solution Approach 1:
The patent applies LFNST to transform coefficients, changing the transformation parameters to achieve better energy compaction. This parameter change concentrates image detail information into fewer non-zero coefficients, preserving image detail while reducing the total data amount requiring compression and transmission
Data Source
AI summary
An image decoding method according to the present document may include the steps of: deriving the position of the final significant coefficient in the current block and transform coefficients for the current block on the basis of residual information; determining whether the index of a sub—block including the final significant coefficient is 0 and whether the position of the final significant coefficient in the sub-block is greater than 0; determining whether a significant coefficient exists in a second area excluding a first area at the upper left end of the current block; and parsing an LFNST index from a bitstream when the position of the final significant coefficient is determined to be greater than 0 in the sub-block in which the index is 0, and the significant coefficient does not exist in the second area.