Non-separable Secondary Transform for Image Coding Efficiency
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images, such as HD and UHD, leads to higher data transmission and storage costs due to increased information volume, necessitating a more efficient image compression technique.
Innovation Solution
A method and apparatus for enhancing image coding efficiency through a non-separable secondary transform (NSST) method, which determines and applies a suitable NSST kernel based on the intra prediction mode and block size to optimize transform coefficients, reducing data volume and improving residual coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality images are transmitted or stored, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies parameter changes by dynamically selecting different transform kernel sizes (4x4 or 8x8) based on the block size and intra prediction mode. This adaptive parameter selection optimizes the transform process to achieve better compression efficiency, reducing the data volume required while maintaining image quality. The NSST index and kernel size are adjusted as parameters to balance between compression ratio and quality preservation.
2Productivity
If a non-separable secondary transform is applied to reduce data volume, then compression efficiency is improved, but transform complexity increases
Solution Approach 1:
The patent implements dynamics by making the transform kernel size selectable based on actual processing needs. The system dynamically chooses between 4x4 and 8x8 kernels depending on the block size and prediction mode, rather than using a fixed complex transform. This dynamic adaptation simplifies the transform process while maintaining high compression efficiency, as the complexity is adjusted according to the specific coding situation.
Solution Approach 2:
The patent changes the transform parameter (kernel size) based on the block size and intra prediction mode. By adjusting this parameter adaptively, the system achieves high compression efficiency without consistently using the most complex transform. The parameter change allows the system to use simpler transforms when appropriate, reducing overall computational complexity while maintaining productivity.
3Productivity
If transform coefficients are concentrated on low-frequency components, then residual coding efficiency is improved, but coding flexibility is reduced
Solution Approach 1:
The patent applies parameter changes by selecting different NSST indices and kernel sizes based on the intra prediction mode and block size. This adaptive parameter selection concentrates transform coefficients on low-frequency components to improve residual coding efficiency. The system adjusts the transform parameters dynamically to match the specific coding situation, maintaining flexibility while achieving concentration of coefficients for better compression.
Data Source
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AI summary
A transform method, according to the present invention, comprises the steps of: obtaining transform coefficients for a target block; determining a non-separable secondary transform (NSST) set for the target block; selecting one of a plurality of NSST kernels included in the NSST set on the basis of an NSST index; and generating modified transform coefficients by non-separable secondary-transform of the transform coefficients on the basis of the NSST kernel that has been selected, wherein the NSST set for the target block is determined on the basis of an intra-prediction mode and/or the size of the target block. According to the present invention, the amount of data transmitted, which is required for residual processing, can be reduced and residual coding efficiency can be increased.