MS-GraphSIM: Inferring Point Cloud Quality via Multiscale Graph Similarity
Yujie Zhang, Qi Yang, Yiling Xu
Abstract
To address the point cloud quality assessment (PCQA) problem, GraphSIM was proposed via jointly considering geometrical and color features, which shows compelling performance in multiple distortion detection. However, GraphSIM does not take into account the mutiscale characteristics of human perception. In this paper, we propose a multiscale PCQA model, called Multiscale Graph Similarity (MS-GraphSIM), that can better predict human subjective perception. First, exploring the multiscale processing method used in image processing, we introduce a multiscale representation of point clouds based on graph signal processing. Second, we extend GraphSIM into multiscale version based on the proposed multiscale representation. Specifically, MS-GraphSIM constructs a multiscale representation for each local patch extracted from the reference point cloud or the distorted point cloud, and then fuses GraphSIM at different scales to obtain an overall quality score. Experiment results demonstrate that the proposed MS-GraphSIM outperforms the state-of-the-art PCQA metrics over two fairly large and independent databases. Ablation studies further prove the proposed MS-GraphSIM is robust to different model hyperparameter settings. The code is available at https://github.com/zyj1318053/MS_GraphSIM.