Quality evaluation of point cloud compression techniques

Abstract

A study on the quality evaluation of point clouds in the presence of coding distortions is presented. For that, four different point cloud coding solutions, notably the standardized MPEG codecs G-PCC and V-PCC, a deep learning-based coding solution RS-DLPCC, and Draco, are compared using a subjective evaluation methodology. Furthermore, several full-reference, reduced-reference and no-reference point cloud quality metrics are evaluated. Two different point cloud normal computation methods were tested for the metrics that rely on them, notably the Cloud Compare quadric fitting method with radius of five, ten, and twenty and Meshlab KNN with K six, ten, and eighteen. To generalize the results, the objective quality metrics were also benchmarked on a public database, with mean opinion scores available. To evaluate the statistical differences between the metrics, the Krasula method was employed. The Point Cloud Quality Metric reveals the best performance and a very good representation of the subjective results, as well as being the metric with the most statistically significant results. It was also revealed that the Cloud Compare quadric fitting method with radius 10 and 20 produced the most reliable normals for the metrics dependent on them. Finally, the study revealed that the most commonly used metrics fail to accurately predict the compression quality when artifacts generated by deep learning methods are present.