A CONVOLUTIONAL NEURAL NETWORK FRAMEWORK FOR BLIND MESH VISUAL QUALITY ASSESSMENT

In this paper, we propose a new method for blind mesh visual quality assessment using a deep learning approach. To do this, we first extract visual representative features by computing locally curvature and dihedral angles from each distorted mesh. Then, we determine from these features a set of 2D patches which are learned to a convolutional neural network (CNN). The network consists of two convolutional layers with two max-pooling layers. Then, a multilayer per-ceptron (MLP) with two fully connected layers is integrated to summarize the learned representation into an output node. With this network structure, feature learning and regression are used to predict the quality score of a given distorted mesh without needing to a reference mesh. Experiments are conducted on LIRIS masking and the general-purpose databases and results show that the trained CNN achieves good rates in terms of correlation with human visual judgment scores.