Adaptive MTS Candidate Selection in Image Transform Decoding
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Solution Overview
Problem
Existing image compression technologies face challenges in efficiently adapting to the statistical characteristics of transform coefficients, leading to increased complexity and reduced encoding efficiency for high-resolution and high-quality images.
Innovation Solution
An adaptive method for determining the range of transform type candidates based on statistical characteristics of transform coefficients in a current block, such as sum, number, position, and non-zero coefficients, to selectively apply Multi-Transform Selection (MTS) for improved encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If MTS is applied to all current blocks, then encoding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the number of MTS candidates based on statistical characteristics of transform coefficients. Different blocks are assigned different numbers of candidates (e.g., 1, 2, or 4 candidates) depending on their complexity metrics, allowing the system to adapt encoding effort to actual content requirements rather than uniformly applying MTS to all blocks.
Solution Approach 2:
The patent implements local quality by applying MTS selectively to specific blocks based on their individual statistical characteristics. Blocks with higher complexity metrics receive more MTS candidates and greater encoding effort, while simpler blocks receive fewer candidates, ensuring that computational resources are concentrated where they provide the most benefit.
2Productivity
If the range of MTS candidates is increased, then encoding efficiency is improved, but the complexity of transform encoding increases
Solution Approach 1:
The patent applies partial action by providing different numbers of MTS candidates to different blocks based on their needs. Rather than providing the maximum number of candidates to all blocks, the system provides only the necessary number for each block, reducing overall complexity while maintaining encoding efficiency where it matters most.
3Productivity
If MTS is adaptively applied based on statistical characteristics, then encoding efficiency is improved, but the complexity of determining transform type candidates increases
Solution Approach 1:
The patent applies preliminary action by calculating statistical characteristics of transform coefficients before selecting MTS candidates. Metrics such as sum of absolute values, number of non-zero coefficients, and position of last significant coefficient are computed in advance to guide the selection process, allowing the system to make informed decisions about candidate allocation without complex real-time analysis.
Data Source
AI summary
An image decoding method and device according to the present disclosure may determine a transform type for inverse transform of a current block from one or more MTS candidates available to the current block, and perform inverse transform on transform coefficients of the current block on the basis of the determined transform type, so as to acquire residual samples of the current block. Here, the number of the one or more MTS candidates available to the current block may be determined on the basis of at least one among a sum of the transform coefficients in the current block, the number of the transform coefficients in the current block, sizes of the one or more transform coefficients in the current block, a location of a last significant coefficient in the current block, and the number of one or more non-zero coefficients in the current block.