A Comparative Measurement Study of Point Cloud-Based Volumetric Video Codecs

摘要

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 com-pute 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 vol-umetric video codecs. In this paper, we conduct a comparative measurement study on five representative point cloud-based volu-metric 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 scenar-ios, 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 addi-tion, 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.

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