FQM-GC: Full-reference Quality Metric for Colored Point Cloud Based on Graph Signal Features and Color Features

Abstract

Colored Point Cloud (CPC) is often distorted in the processes of its acquisition, processing, and compression, so reliable quality assessment metrics are required to estimate the perception of distortion of CPC. We propose a Full-reference Quality Metric for colored point cloud based on Graph signal features and Color features (FQM-GC). For geometric distortion, the normal and coordinate information of the sub-clouds divided via geometric segmentation is used to construct their underlying graphs, then, the geometric structure features are extracted. For color distortion, the corresponding color statistical features are extracted from regions divided with color attribution. Meanwhile, the color features of different regions are weighted to simulate the visual masking effect. Finally, all the extracted features are formed into a feature vector to estimate the quality of CPCs. Experimental results on three databases (CPCD2.0, IRPC and SJTU-PCQA) show that the proposed metric FQM-GC is more consistent with human visual perception.