No-Reference 3D Mesh Quality Assessment Based on Dihedral Angles Model and Support Vector Regression

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• 关键词：No ; reference mesh quality assessment ; Support vector regression ; Dihedral angles ; Gamma distribution ; Visual masking effect
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9680
• 期：1
• 页码：369-377
• 全文大小：1,119 KB
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• 作者单位：Ilyass Abouelaziz (19)
  Mohammed El Hassouni (19)
  Hocine Cherifi (20)
  
  19. LRIT URAC 29, Mohammed V University in Rabat, Rabat, Morocco
  20. LE2I, UMR 6306 CNRS, University of Burgundy, Dijon, France
  
• 丛书名：Image and Signal Processing
• ISBN：978-3-319-33618-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9680

文摘

3D meshes are subject to various visual distortions during their transmission and geometrical processing. Several works have tried to evaluate the visual quality using either full reference or reduced reference approaches. However, these approaches require the presence of the reference mesh which is not available in such practical situations. In this paper, the main contribution lies in the design of a computational method to automatically predict the perceived mesh quality without reference and without knowing beforehand the distortion type. Following the no-reference (NR) quality assessment principle, the proposed method focuses only on the distorted mesh. Specifically, the dihedral angles are firstly computed as a surface roughness indexes and so a structural information descriptors. Then, a visual masking modulation is applied to this angles according to the main characteristics of the human visual system. The well known statistical Gamma model is used to fit the dihedral angles distribution. Finally, the estimated parameters of the model are learned to the support vector regression (SVR) in order to predict the quality score. Experimental results demonstrate the highly competitive performance of the proposed no-reference method relative to the most influential methods for mesh quality assessment.