Perceptual Quality Assessment for Point Clouds:A Survey

A point cloud is considered a promising 3D representation that has achieved wide applications in several fields.However,quality degradation inevitably occurs during its acquisition and generation,communication and transmission,and rendering and display.Therefore,how to accurately perceive the visual quality of point clouds is a meaningful topic.In this survey,we first introduce the point cloud to emphasize the importance of point cloud quality assessment(PCQA).A review of subjective PCQA is followed,including common point cloud distortions,subjective experimental setups and subjective databases.Then we review and compare objective PCQA methods in terms of modelbased and projection-based.Finally,we provide evaluation criteria for objective PCQA methods and compare the performances of various methods across multiple databases.This survey provides an overview of classical methods and recent advances in PCQA.

基于v-SVR的无参考3D点云质量评估模型

为了实现对3D点云质量有效监控,提出一种基于v-SVR的无参考3D点云质量评估模型。首先,分析失真点云编码相关参数与主观质量之间的关系,确定编码设置对主观质量的影响。其次,分析相同编码设置下不同内容特性对主观质量的影响,并提出了一个能够估计表征点云内容特性的几何特性因子和纹理特性因子的模型。最后,将量化参数、位置量化尺度、纹理特征因子、几何特征因子作为v-SVR的输入参数,主观质量分数作为输出参数,训练并得出一个反映人眼视觉特性的无参考3D点云质量评估模型。实验结果表明,与其他典型点云质量评估模型(Pointssim、PSNR-Y、PCMrr)相比,本文模型得到客观质量分数与主观质量分数的皮尔森相关系数分别提高了0.3265、0.1855、0.1748,均方根误差分别下降了6.3174、3.8350、3.6050。