A Comparative Measurement Study of Point Cloud-Based Volumetric Video Codecs
Mengyu Yang, Zhenxiao Luo, Miao Hu, Min Chen, Di Wu
Abstract
Volumetric video is fully three-dimensional and immersive, which shows great potential in various application scenarios. However, the naive delivery of volumetric video over the Internet requires a huge amount of bandwidth and compute resources. A volumetric video should be compressed by a specific codec before transmission. In spite of the existence of a few pioneering efforts on prototype design, there is very limited work to investigate the efficiency and practicality of existing volumetric video codecs. In this paper, we conduct a comparative measurement study on five representative point cloud-based volumetric video codecs, including 3 conventional codecs (e.g., Draco, G-PCC, V-PCC) and 2 neural-based codecs (e.g., PU-GCN+, MPU+), on six real volumetric video datasets. We investigate the applicability of the above codecs in different application scenarios, and examine how the features of point cloud (e.g., quality, texture, geometric and scene complexity) affect the quality of compressed volumetric video from multiple perspectives. In addition, we also study the impact of users’ viewing behaviors on the coding and delivery efficiency with Quality of Experience (QoE) metrics. Our results shed a number of important insights, which provide useful guidelines for optimizing the design of future volumetric video streaming systems.