V-PCC Projection Based Blind Point Cloud Quality Assessment for Compression Distortion

Abstract

In immersive media communication with point cloud (PC), PC compression will inevitably cause distortion, which may affect quality of users’ visual experience. To monitor the visual quality of PCs, point cloud quality assessment (PCQA) metrics are highly desired. Inspired by the video-based point cloud compression (V-PCC) standard to project a PC to planes and quantify the corresponding projection maps, a new blind PCQA metric based on V-PCC texture projection map and geometry projection map of PC is proposed for evaluating PC with compression distortion. Specifically, considering the combination of texture and geometry features of the projection maps to characterize distorted PC, a dual-stream convolutional network is designed from the perspective of global and local feature description to extract texture and geometry features of the distorted PC. The network mainly consists of two modules, that is, global texture and geometry fusion (GTGF) module and local geometry weighted local texture (LGWLT) module. The GTGF module is designed to realize the fusion of global texture and geometry features, which can describe PC's combination effect of texture and geometry distortions. Aiming at the relationship between texture and geometry distortions, the LGWLT module is designed to extract the features of local texture distortion caused by local geometry distortion. Furthermore, visual perception is combined to take distortions of the PC's salient regions into account in the process of PCQA. The experimental results on three PC databases show that the proposed PCQA metric is more consistent with visual perception, thus has greater potential than the representative PCQA metrics.