No-Reference Textured Mesh Quality Assessment Using Graph-Based Features

3D mesh models with texture maps play an important role in various 3D applications. Since No-Reference Textured Mesh Quality Assessment (NR-TMQA) methods are essential to identify low-quality mesh models and enhance their quality, this paper presents an accurate NR-TMQA method. Our key contributions are as follows: 1) We propose a novel NR-TMQA scheme that leverages both point cloud and rendered image representations from distorted mesh models. 2) We introduce a novel point cloud representation derived from the faces of a 3D mesh model. It is referred to as a face-based point cloud, which effectively captures face-level features. 3) We present novel graph-based features to estimate mesh quality using the face-based point cloud. Our experimental results demonstrate that Pearson’s linear correlation coefficient and Spearman’s rank-order correlation coefficient in two large-scale TMQA datasets improved by 0.148 (from 0.572 to 0.720) and 0.18 (from 0.525 to 0.705), respectively, compared to the conventional state-of-the-art TMQA method.