Abstract
With the wide applications of three-dimensional (3D) meshes in intelligent manufacturing, digital animation, virtual reality, digital cities and other fields, more and more processing techniques are being developed for 3D meshes, including watermarking, compression, and simplification, which will inevitably lead to various distortions. Therefore, how to evaluate the visual quality of 3D mesh is becoming an important problem and it is necessary to design effective tools for blind 3D mesh quality assessment. In this paper, we propose a new Blind Mesh Quality Assessment method based on Graph Spectral Entropy and Spatial features, called as BMQA-GSES. 3D mesh can be represented as graph signal, in the graph spectral domain, the Gaussian curvature signal of the 3D mesh is firstly converted with Graph Fourier transform (GFT), and then the smoothness and information entropy of amplitude features are extracted to evaluate the distortion. In the spatial domain, four well-performing spatial features are combined to describe the concave and convex information and structural information of 3D meshes. All the extracted features are fused by the random forest regression to predict the objective quality score of the 3D mesh. Experiments are performed successfully on the public databases and the obtained results show that the proposed BMQA-GSES method provides good correlation with human visual perception and competitive scores compared to state-of-art quality assessment methods.
Highlights
As three-dimensional (3D) meshes can provide very realistic visual information for users, the applications of 3D mesh in intelligent manufacturing, digital animation, virtual reality, digital cities, and other fields are becoming more and more widespread [1]
We propose a new blind mesh quality assessment (BMQA) method based on Graph Spectral Entropy and spatial features, referred to as BMQA-GSES
To verify the effectiveness of the proposed BMQA-GSES method, a series of experiments were performed in the public LIRIS/EPFL general-purpose database [11], which has been widely used for performance testing of 3D mesh quality assessment (MQA) methods
Summary
As three-dimensional (3D) meshes can provide very realistic visual information for users, the applications of 3D mesh in intelligent manufacturing, digital animation, virtual reality, digital cities, and other fields are becoming more and more widespread [1]. 3D meshes can be compressed, simplified, embedded with watermarks, etc., in practical applications [2,3], which inevitably leads to visual distortions of the 3D mesh. How to better assess 3D mesh visual quality of the 3D mesh to provide a standard for measuring the performance of compression, simplification and other technologies is a critical problem that needs to be solved [4]. Subjective assessment results are close to the subjective visual perception of human eyes, but this method is time-consuming, laborious, too expensive, and prone to cause errors due to artificial uncertainties. It is necessary to propose an objective quality assessment method that has good consistency with the subjective perception of human eyes
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