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Related Concept Videos

Mesh Analysis01:20

Mesh Analysis

1.2K
Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Blind Mesh Assessment Based on Graph Spectral Entropy and Spatial Features.

Yaoyao Lin1, Mei Yu1, Ken Chen1

  • 1Faculty of Information Science and Engineering, Ningbo University, No. 818, Ningbo 315211, China.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study introduces a novel Blind Mesh Quality Assessment (BMQA) method using Graph Spectral Entropy and Spatial features (BMQA-GSES) to evaluate 3D mesh distortions. The BMQA-GSES method effectively predicts visual quality, correlating well with human perception.

Keywords:
blind mesh quality assessmentgraph signal processinggraph spectral entropy featuresspatial features

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Area of Science:

  • Computer Graphics and Vision
  • Digital Signal Processing
  • Geometric Modeling

Background:

  • Three-dimensional (3D) meshes are crucial in diverse fields like manufacturing, animation, and virtual reality.
  • Processing techniques such as watermarking, compression, and simplification introduce distortions in 3D meshes.
  • Effective blind 3D mesh quality assessment is essential due to inevitable distortions.

Purpose of the Study:

  • To propose a new Blind Mesh Quality Assessment (BMQA) method.
  • To develop a method that accurately predicts the visual quality of distorted 3D meshes.
  • To provide an objective quality score that correlates with human visual perception.

Main Methods:

  • Representing 3D meshes as graph signals for analysis.
  • Utilizing Graph Fourier Transform (GFT) on Gaussian curvature signals in the graph spectral domain.
  • Extracting smoothness and information entropy from amplitude features, combined with spatial features (concave/convex and structural information), and fused via random forest regression.

Main Results:

  • The proposed BMQA-GSES method achieved good correlation with human visual perception.
  • Experimental results on public databases demonstrate competitive performance compared to state-of-the-art methods.
  • The fusion of spectral and spatial features effectively captures mesh distortions.

Conclusions:

  • The BMQA-GSES method offers an effective approach for blind 3D mesh quality assessment.
  • The integration of graph spectral entropy and spatial features provides a robust quality prediction.
  • This method is valuable for applications requiring objective evaluation of 3D mesh visual quality.