Learning Graph Convolutional Network for Blind Mesh Visual Quality Assessment

Learning Graph Convolutional Network for Blind Mesh Visual Quality Assessment

This paper proposes a new method for blind mesh visual quality assessment (MVQA) based on a graph convolutional network. For that, we address the node classification problem to predict the perceived visual quality. First, two matrices representing the 3D mesh are considered: a graph adjacency matrix...

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Bibliographic Details
Main Authors: Ilyass Abouelaziz, Aladine Chetouani, Mohammed El Hassouni, Hocine Cherifi, Longin Jan Latecki
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Mesh visual quality assessment
graph convolutional networks
mesh graph representation
geometric attributes
Online Access:https://ieeexplore.ieee.org/document/9474332/

Internet

https://ieeexplore.ieee.org/document/9474332/

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