No-Reference Mesh Visual Quality Assessment Using Graph-Based Deep Learning-Reference-Cited by-同舟云学术

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No-Reference Mesh Visual Quality Assessment Using Graph-Based Deep Learning

Published:2021-10-06 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)
language:
Short-container-title:

Author:

Abouelaziz Ilyass¹,Chetouani Aladine²,El Hassouni Mohammed³,Cherifi Hocine⁴

Affiliation:

1. L@Bisen,Yncrea Ouest, 20 rue Cuirassé Bretagne,Brest,France,29200

2. University of Orleans,Prisme Laboratory,Orleans,France

3. Mohammed V University in Rabat,FLSH,Rabat,Morocco

4. University of Burgundy,LIB EA 7534,Dijon,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9733399/9733432/09733513.pdf?arnumber=9733513

Reference22 articles.

1. MESH: measuring errors between surfaces using the Hausdorff distance

2. A Multiscale Metric for 3D Mesh Visual Quality Assessment

3. A Curvature Tensor Distance for Mesh Visual Quality Assessment

4. Watermarked 3-D Mesh Quality Assessment

5. A fast roughness-based approach to the assessment of 3D mesh visual quality

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Graph-based No-Reference Video Quality Assessment Using Spatial Features;2024 International Conference on Signal Processing and Communications (SPCOM);2024-07-01

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