Graph-Based Transform Kernel Normalization for Video Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Graph-based signal processing for video compression faces challenges in achieving efficient signal transformation and prediction while minimizing overhead, particularly in requiring synchronized graph structures for encoding and decoding, and dealing with excessive bit rate overhead.
Innovation Solution
A method is introduced to generalize graph parameters, derive optimized transform kernels using penalty functions, and detect vertices and edges in graphs for encoding or decoding residual signals, thereby improving compression efficiency and controlling transform properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If more complex and adaptive graph structure is used, then signal compression performance is improved, but coding overhead becomes greater
Solution Approach 1:
The patent applies parameter changes by introducing a scaling factor that transforms the graph Laplacian matrix into a normalized form. This normalization process modifies the spectral properties of the graph while preserving its structural information, enabling efficient transform operations without requiring complex adaptive graph structures. The scaling factor adjustment allows the system to achieve good compression performance with simpler graph representations.
Solution Approach 2:
The patent segments the graph-based transform process into distinct components: graph construction, spectral decomposition, transform kernel generation, and residual encoding. By separating these functions, the system can use a standardized graph structure for the transform while handling complexity only where necessary in the residual processing stage, thus reducing overall coding overhead.
2Productivity
If graph-based transform is applied, then signal transformation is improved, but sharp discontinuity problem occurs
Solution Approach 1:
The patent resolves the sharp discontinuity problem by applying parameter changes to the graph Laplacian matrix through normalization. The normalized Laplacian matrix has spectral properties that ensure smooth transform kernels, eliminating the sharp discontinuities that occur with unnormalized graph Laplacians. This normalization process modifies the eigenvalues and eigenvectors to produce continuous and stable transform operations.
3Adaptability or versatility
If generalized graph parameters are used, then transform properties are better controlled, but computational complexity increases
Solution Approach 1:
The patent controls transform properties by introducing a scaling factor that normalizes the graph Laplacian matrix. This normalization process modifies the spectral properties in a controlled manner, allowing adjustment of transform characteristics through a single parameter rather than requiring complex generalization of graph parameters. The approach maintains computational efficiency while providing versatile control over transform properties.
Data Source
AI summary
The present invention provides a method for decoding a video signal using a graph-based transform including receiving a generalized graph signal including a graph parameter set; obtaining a graph-based transform kernel of a transform unit based on the graph parameter set and a predetermined penalty function; and decoding the transform unit using the graph-based transform kernel.