Reduced reference 3D mesh quality assessment based on statistical models

During their geometry processing and transmission 3D meshes are subject to various visual processing operations like compression, watermarking, remeshing, noise addition and so forth. In this context it is indispensable to evaluate the quality of the distorted mesh, we talk here about the mesh visual quality (MVQ) assessment. Several works have tried to evaluate the MVQ using simple geometric measures, However this metrics do not correlate well with the subjective score since they fail to reflect the perceived quality. In this paper we propose a new objective metric to evaluate the visual quality between a mesh with a perfect quality called reference mesh and its distorted version. The proposed metric uses a chosen statistical distribution to extract parameters of two random variable sets, the first set is the dihedral angles related to the reference mesh, while the second set is the dihedral angles related to the distorted mesh. The perceptual distance between two meshes is computed as the Kullback-Leibler divergence between the two sets of variables. Experimental results from two subjective databases (LIRIS masking database and LIRIS/EPFL general purpose database) and comparisons with seven objective metrics cited in the state-of-the-art demonstrate the efficacy of the proposed metric in terms of the correlation to the mean opinion scores across these databases.