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

We propose in this work a graph-based deep learning method for mesh visual quality assessment. To carry out this proposal, we transform a given distorted mesh to a graph represented by its adjacency matrix, and extract a set of geometric and perceptual features to be stored into a feature matrix. The two matrices are then learned to a graph convolutional network (GCN). The network is composed by two convolutional layers followed by a max-pooling layer. The Softmax classifier is used to predict the quality relying on the node classification problem. Five classes are considered according to the ground truth scores: very bad, bad, medium, good or excellent quality. Experiments are conducted on two publicly available databases specifically constructed for the quality assessment task. Our method is compared to some influential and effective full and reduced reference methods. The good performance is proven by the excellent correlations with subjective decisions.