Adaptive Video Transform Phase Tuning for Sparse Residual Coding
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Solution Overview
Problem
Existing video encoding and decoding methods do not effectively leverage sparsity in prediction residuals to improve compression efficiency, leading to increased bit usage and reconstruction distortion.
Innovation Solution
Adaptive transform technique that adjusts the phase factors of transform basis functions based on sparsity measures, using reconstructed prediction residuals to optimize transform coefficients for improved sparsity and compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed transform function is used for all blocks, then the encoding process is simple and fast, but compression efficiency is reduced due to inability to exploit sparsity in prediction residuals
Solution Approach 1:
The patent applies dynamics by making the transform function adaptive rather than fixed. The transform type is dynamically selected based on the sparsity characteristics of prediction residuals, allowing the system to adapt to different block characteristics and maximize compression efficiency while maintaining manageable complexity through automated selection criteria.
Solution Approach 2:
The patent changes the transform parameters (type and phase factors) based on measured sparsity characteristics of the prediction residuals. By analyzing the sparsity parameter and adjusting transform accordingly, the system improves compression efficiency without requiring manual intervention, resolving the contradiction between efficiency and complexity.
2Loss of information
If transform phase factors are not adjusted, then the encoding process is computationally simple, but the number of non-zero transform coefficients increases leading to higher bit usage
Solution Approach 1:
The patent adjusts transform phase factors based on the sparsity characteristics of prediction residuals to minimize the number of non-zero coefficients. This parameter adjustment reduces bit usage for encoding while the automated selection process based on sparsity measures keeps the computational complexity manageable.
Solution Approach 2:
The patent replaces manual or complex optimization methods with an automated sparsity-based selection mechanism. By using sparsity measures as a proxy for optimal transform selection, the system achieves better compression (reduced bit usage) without proportionally increasing computational complexity.
3Productivity
If adaptive transform with sparsity-based phase factor selection is applied, then compression efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent uses sparsity measures as a computationally efficient proxy to guide transform selection and phase factor adjustment. By basing adaptive transform decisions on sparsity characteristics rather than exhaustive optimization, the system improves compression efficiency while keeping computational overhead reasonable.
Solution Approach 2:
The patent replaces computationally intensive optimal transform selection with a sparsity-based decision mechanism. This substitution maintains improved compression efficiency while significantly reducing the computational energy required compared to full optimization approaches.
Data Source
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AI summary
For a picture with two or more color components, the prediction residuals for the first color component of a block to be encoded may be transformed with a first transform. The transform coefficients for the first color component may go through quantization, de-quantization and inverse transform to obtain reconstructed prediction residuals. Based on the reconstructed prediction residuals for the first color component, the phases of the transform basis function of the first transform can be adjusted to improve the sparsity of the transformed signal. The prediction residuals for the remaining color components may then be transformed with the adjusted transform. In order to determine the phase shift factor, the reconstructed prediction residuals for the first color component may be transformed with the first transform, adjusted by different candidate phase shift factors, and the candidate phase shift factor that provides a smallest sparsity measure can be selected for the block.