Performance Analysis of Deep Learning-Based Lossy Point Cloud Geometry Compression Coding Solutions

The quality evaluation of three deep learning-based coding solutions for point cloud geometry, notably ADLPCC, PCC GEO CNNv2, and PCGCv2, is presented. The MPEG G-PCC was used as an anchor. Furthermore, LUT SR, which uses multi-resolution Look-Up tables, was also considered. A set of six point clouds representing landscapes and objects was used. As point cloud texture has a great influence on the perceived quality, two different subjective studies that differ in the texture addition model are reported and statistically compared. In the first experiment, the dataset was first encoded with the identified codecs. Then, the texture of the original point cloud was mapped to the decoded point cloud using the Meshlab software, resulting in a point cloud with both geometry and texture information. Finally, the resulting point cloud was encoded with G-PCC using the lossless-geometry-lossy-atts mode, while in the second experiment the texture was mapped directly onto the distorted geometry. Moreover, both subjective evaluations were used to benchmark a set of objective point cloud quality metrics. The two experiments were shown to be statistically different, and the tested metrics revealed quite different behaviors for the two sets of data. The results reveal that the preferred method of evaluation is the encoding of texture information with G-PCC after mapping the texture of the original point cloud to the distorted point cloud. The results suggest that current objective metrics are not suitable to evaluate distortions created by machine learning-based codecs. Finally, this paper presents a study on the compression performance stability of the tested machine learning-based codecs using different training sessions. The obtained results show that the tested codecs revealed a high level of stability across all training sessions for most of the content, although some undesirable exceptions may be found.