Quality Assessment and Modeling for MPEG V-PCC Volumetric Video

Volumetric video, which is typically represented by 3D point clouds, requires efficient point cloud compression (PCC) technologies for practical storage and transmission. Particularly, developed by the Moving Picture Experts Group (MPEG), video-based PCC (V-PCC) converts 3D point clouds into 2D image maps and compresses them with 2D video codecs, showing excellent compression performance. However, understanding the impact of compression on the perceptual quality of volumetric videos, which consist of both geometry and texture components, remains challenging. In this study, we propose a quality of experience (QoE) model to predict the subjective quality with respect to the compression level of geometry and texture, quantifying the impact of geometry and texture compression on perceptual quality. To the best of our knowledge, this study is the first to accurately model the perceptual quality of V-PCC-encoded volumetric videos. The QoE model is built based on a volumetric video quality assessment dataset, VOLVQAD, collected by us. We further evaluate our QoE model on the vsenseVVDB2 dataset, which was collected from diverse study settings, to validate its robustness and generalization ability. Both evaluations demonstrate the effectiveness of our model in various compression scenarios. This study makes a valuable contribution to our understanding of the factors that influence the QoE in V-PCC-encoded volumetric videos. The proposed model also holds potential for various other applications, such as adaptive bitrate allocation.