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

Ilyass Abouelaziz, 1, * Mohammed El Hassouni 1, * Hocine Cherifi 2, *
* Auteur correspondant
1 Faculté des sciences [Rabat]
2 Le2i - Laboratoire Electronique, Informatique et Image
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.
Keywords : computational geometry learning (artificial intelligence) mesh generation neural nets regression analysis INSPEC: Non-Controlled Indexing curvature based method blind mesh visual quality assessment no-reference quality assessment computational method human perceived quality prediction distorted mesh GRNN mean curvature perceptual feature visual aspect representation 3D mesh human subjective scores general regression neural network training visual quality subject scores competitive scores Author Keywords Blind mesh visual quality assessment general regression neural network mean opinion scores predicted objective scores feature learning
Type de document :
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〉
Liste complète des métadonnées
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

Identifiants

Collections

LE2I

Citation

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〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

12

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD