Assessing objective quality metrics for JPEG and MPEG point cloud coding

As applications using immersive media gained increased attention from both academia and industry, research in the field of point cloud compression has greatly intensified in recent years, leading to the development of the MPEG compression standards V-PCC and G-PCC, as well as the more recent JPEG Pleno learning-based point cloud coding. Each of the standards mentioned above is based on a different algorithm, introducing distinct types of degradation that may impair the quality of experience when lossy compression is applied. Although the impact on perceptual quality can be accurately evaluated during subjective quality assessment experiments, objective quality metrics also predict the visually perceived quality and provide similarity scores without human intervention. Nevertheless, their accuracy can be susceptible to the characteristics of the evaluated media as well as to the type and intensity of the added distortion. While the performance of multiple state-of-the-art objective quality metrics has already been evaluated through their correlation with subjective scores obtained in the presence of artifacts produced by the MPEG standards, no study has evaluated how metrics perform with the more recent JPEG Pleno point cloud coding. In this paper, a study is conducted to benchmark the performance of a large set of objective quality metrics in a subjective dataset including distortions produced by JPEG and MPEG codecs. The dataset also contains three different trade-offs between color and geometry compression for each codec, adding another dimension to the analysis. Performance indexes are computed over the entire dataset but also after splitting according to the codec and to the original model, resulting in detailed insights about the overall performance of each visual quality predictor as well as their cross-content and cross-codec generalization ability.