A Curvature Based Method for Blind Mesh Visual Quality Assessment Using a General Regression Neural Network

Abstract : No-reference quality assessment is a challenging issue due to the non-existence of any information related to the reference and the unknown distortion type. The main goal is to design a computational method to objectively predict the human perceived quality of a distorted mesh and deal with the practical situation when the reference is not available. In this work, we design a no reference method that relies on the general regression neural network (GRNN). Our network is trained using the mean curvature which is an important perceptual feature representing the visual aspect of a 3D mesh. Relatively to the human subjective scores, the trained network successfully assesses the visual quality, in addition, the experimental results show that the proposed method provides good correlations with the subject scores and competitive scores comparing to some influential and effective full and reduced reference existing metrics.
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Communication dans un congrès
12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2016 , Nov 2016, Naples, Italy. IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, Signal-Image Technology & Internet-Based Systems (SITIS), 2016 12th International Conference on, 2017, 〈http://ieeexplore.ieee.org/document/7907557/〉. 〈10.1109/SITIS.2016.130〉
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01557927
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : jeudi 6 juillet 2017 - 16:26:47
Dernière modification le : mardi 6 février 2018 - 15:56:16

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Ilyass Abouelaziz,, Mohammed El Hassouni, Hocine Cherifi. A Curvature Based Method for Blind Mesh Visual Quality Assessment Using a General Regression Neural Network. 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2016 , Nov 2016, Naples, Italy. IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, Signal-Image Technology & Internet-Based Systems (SITIS), 2016 12th International Conference on, 2017, 〈http://ieeexplore.ieee.org/document/7907557/〉. 〈10.1109/SITIS.2016.130〉. 〈hal-01557927〉

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