Layered Givens Transform for Video Compression Complexity
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Solution Overview
Problem
Current video compression technologies face challenges in efficiently processing next-generation video content with high spatial resolution, high frame rate, and high dimensionality, requiring transforms with low computational complexity while maintaining compression performance similar to high-complexity target transforms.
Innovation Solution
A method using a layered Givens transform (LGT) is proposed, which approximates a given target transform by deriving rotation and permutation layers through optimization processes, including the Hungarian and blossom algorithms, to reduce computational complexity while maintaining compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a non-separable transform (e.g., KLT) is used to improve compression performance, then compression efficiency is improved, but computational complexity increases significantly
Solution Approach 1:
The transform is segmented into multiple layers, where each layer applies a simplified Givens rotation to specific coefficient groups. This segmentation allows the complex non-separable transform to be approximated through a series of simpler operations, reducing overall computational complexity while maintaining compression performance.
Solution Approach 2:
The invention changes the parameter representation by using Givens rotation angles and sparsity patterns to define the transform, rather than using full transformation matrices. This parameterization enables more efficient computation and allows for adaptive selection of transform characteristics based on signal properties.
2Productivity
If a separable transform (e.g., DCT) is used to reduce computational complexity, then processing speed is improved, but compression performance deteriorates
Solution Approach 1:
The transform is segmented into multiple layers, where each layer applies a simplified Givens rotation to specific coefficient groups. This segmentation allows the complex non-separable transform to be approximated through a series of simpler operations, reducing overall computational complexity while maintaining compression performance.
3Measurement precision
If a complex transform is designed to handle variable statistical properties of signal blocks, then compression performance is improved, but device complexity increases
Solution Approach 1:
The transform structure is made dynamic and adaptive through the use of mode-dependent transform selection and adaptive parameter optimization. Different transform configurations (varying numbers of layers, rotation angles, and sparsity patterns) are selected based on the statistical properties of the input signal block, allowing the system to adapt to varying signal characteristics without requiring a fully complex transform in all cases.
Data Source
AI summary
The present invention provides a method for performing transformation using a layered Givens transform (LGT), comprising the steps of: deriving at least one rotation layer and at least one permutation layer on the basis of a given transform matrix (H) and a given error parameter; acquiring a LGT coefficient on the basis of the rotation layer and the permutation layer; and quantizing and entropy-encoding the LGT coefficient, wherein the permutation layer comprises a permutation matrix obtained by permuting a row of an identity matrix.