Support Vector Regression-based Reduced-Reference Perceptual Quality Model for Compressed Point Clouds

Video-based point cloud compression (V-PCC) is a state-of-the-art moving picture experts group (MPEG) standard for point cloud compression. V-PCC can be used to compress both static and dynamic point clouds in a lossless, near lossless, or lossy way. Many objective quality metrics have been proposed for distorted point clouds. Most of these metrics are full-reference metrics that require both the original point cloud and the distorted one. However, in some real-time applications, the original point cloud is not available, and no-reference or reduced-reference quality metrics are needed. Three main challenges in the design of a reduced-reference quality metric are how to build a set of features that characterize the visual quality of the distorted point cloud, how to select the most effective features from this set, and how to map the selected features to a perceptual quality score. We address the first challenge by proposing a comprehensive set of features consisting of compression, geometry, normal, curvature, and luminance features. To deal with the second challenge, we use the least absolute shrinkage and selection operator (LASSO) method, which is a variable selection method for regression problems. Finally, we map the selected features to the mean opinion score in a nonlinear space. Although we have used only 19 features in our current implementation, our metric is flexible enough to allow any number of features, including future more effective ones. Experimental results on the Waterloo point cloud dataset version 2 (WPC2.0) and the MPEG point cloud compression dataset (M-PCCD) show that our method, namely PCQAML, outperforms state-of-the-art full-reference and reduced-reference quality metrics in terms of Pearson linear correlation coefficient, Spearman rank order correlation coefficient, Kendall's rank-order correlation coefficient, and root mean squared error.