CONVOLUTIONAL NEURAL NETWORK FOR BLIND MESH VISUAL QUALITY ASSESSMENT USING 3D VISUAL SALIENCY
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | CONVOLUTIONAL NEURAL NETWORK FOR BLIND MESH VISUAL QUALITY ASSESSMENT USING 3D VISUAL SALIENCY |
| Type de publication | Conference Paper |
| Year of Publication | 2018 |
| Auteurs | Abouelaziz I, Chetouani A, Hassouni MEl, Latecki LJan, Cherifi H |
| Conference Name | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
| Publisher | Inst Elect & Electron Engneers; IEEE Signal Processing Soc |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-4799-7061-2 |
| Mots-clés | blind mesh visual quality assessment, Convolutional Neural Network (CNN), Mesh visual saliency |
| Résumé | In this work, we propose a convolutional neural network (CNN) framework to estimate the perceived visual quality of 3D meshes without having access to the reference. The proposed CNN architecture is fed by small patches selected carefully according to their level of saliency. To do so, the visual saliency of the 3D mesh is computed, then we render 2D projections from the 3D mesh and its corresponding 3D saliency map. Afterward, the obtained views are split to obtain 2D small patches that pass through a saliency filter to select the most relevant patches. Experiments are conducted on two MVQ assessment databases, and the results show that the trained CNN achieves good rates in terms of correlation with human judgment. |