Pseudo-Reference Point Cloud Quality Measurement Based on Joint 2-D and 3-D Distortion Description

Renwei Tu, Gangyi Jiang, Mei Yu, Yun Zhang, Ting Luo, Zhongjie Zhu

Abstract: Point cloud (PC) compression inevitably introduces distortion during communication, which can affect users’ visual experience. Thus, efficient PC quality measurement (PCQM) tools are highly desired to measure the PC’s visual quality. In this article, a pseudo-reference PCQM metric based on joint 2-D and 3-D distortion description is proposed. In 2-D description, aiming at the visual quality degradation reflected cooperatively by PC’s texture distortion and geometry distortion, a joint texture-geometry distribution with texture projection map and geometry projection map of the video-based PC compression (V-PCC) standard is constructed to measure the joint texture-geometry distortion of PC. Since the geometry distortion of PC results in the similar distortion phenomena in the geometry projection map and texture projection map, a self-reference geometry-texture structural similarity (SGT-SSIM) is proposed. The separate statistical features of texture projection map and geometry projection map are also considered. In 3-D description, considering the limitations of using full-reference (FR) metric and the difficulty of directly reflecting PC cracks and outliers only by the V-PCC projection, a pseudo-reference PC is constructed by performing Poisson surface reconstruction on the distorted PC. Then, the point-to-distribution (P2D) is used to directly characterize pseudo-referenced geometry distortion, while gray-level-gradient co-occurrence matrix (GGCM) based on key points of PC is constructed to measure the texture distortion. Finally, the features with joint 2-D and 3-D distortion description are combined to measure the PC visual quality more comprehensively. Experimental results on five PC datasets demonstrate that the proposed metric has comparable performance to the existing FR metrics.