A novel spatial pooling method for 3D mesh quality assessment based on percentile weighting strategy

Most of existing mesh quality assessment metrics consist of a similar two-stage computation process: first constructing a quality map by comparing the local regions between reference mesh and distorted mesh, then adopting a spatial pooling method to generate the overall quality score. In this paper, we propose a novel spatial pooling method for 3D mesh quality assessment based on percentile weighting strategy. We assign more weight to the severely distorted regions of the mesh at the pooling stage and extend the percentile weighting method by incorporating surface area at the pooling stage. Our analysis indicates that the percentile weighting method has a strong capability to emphasize the local regions with severe distortion of the mesh. We develop a mesh quality metric by pooling the local distances generated by the Tensor-based Perceptual Distance Measure metric with our spatial pooling method. We investigate the influence of the parameters of percentile weighting on the performance, and determine the optimal parameters and unified parameters through empirical tests on three publicly available databases. Experimental results demonstrate the effectiveness of our spatial pooling method and the superiority of our metric over state-of-the-art metrics. (C) 2018 Elsevier Ltd. All rights reserved.