Lin Yaoyao, Yu Mei, Chen Ken, Jiang Gangyi, Chen Fen, Peng Zongju
Faculty of Information Science and Engineering, Ningbo University, No. 818, Ningbo 315211, China.
Entropy (Basel). 2020 Feb 7;22(2):190. doi: 10.3390/e22020190.
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.
随着三维（3D）网格在智能制造、数字动画、虚拟现实、数字城市等领域的广泛应用，针对3D网格的处理技术不断涌现，包括水印、压缩和简化等，这些处理不可避免地会导致各种失真。因此，如何评估3D网格的视觉质量成为一个重要问题，有必要设计有效的工具用于盲3D网格质量评估。在本文中，我们提出了一种基于图谱熵和空间特征的新型盲网格质量评估方法，称为BMQA-GSES。3D网格可表示为图信号，在图谱域中，首先通过图傅里叶变换（GFT）对3D网格的高斯曲率信号进行转换，然后提取幅度特征的平滑度和信息熵以评估失真。在空间域中，结合四个性能良好的空间特征来描述3D网格的凹凸信息和结构信息。所有提取的特征通过随机森林回归进行融合，以预测3D网格的客观质量得分。在公共数据库上成功进行了实验，所得结果表明，与现有质量评估方法相比，所提出的BMQA-GSES方法与人类视觉感知具有良好的相关性且得分具有竞争力。
