2021/11/04 by Jie Li, Li, Jie, Xiao Wang +5
Computer Science · Engineering · #Cloud computing #Computer network #Computer science #Computer vision #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Image and Video Quality Assessment #Metric (unit) #Multimedia #Multimedia (cs.MM) #Point (geometry) #Point cloud #Quality of experience #Quality of service #Real-time computing #Video streaming #Virtual Reality Applications and Impacts #Visual Attention and Saliency Detection #cs.MM #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2111.02985
The thesis still needs to be revised. There are some problems in the structure of the thesis
openalex publication_date 2021/11/04 · arxiv created 2022/01/21 · arxiv updated 2022/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Point cloud video has been widely used by augmented reality (AR) and virtual reality (VR) applications as it allows users to have an immersive experience of six degrees of freedom (6DoFs). Yet there is still a lack of research on quality of experience (QoE) model of point cloud video streaming, which cannot provide optimization metric for streaming systems. Besides, position and color information contained in each pixel of point cloud video, and viewport distance effect caused by 6DoFs viewing procedure make the traditional objective quality evaluation metric cannot be directly used in point cloud video streaming system. In this paper we first analyze the subjective and objective factors related to QoE model. Then an experimental system to simulate point cloud video streaming is setup and detailed subjective quality evaluation experiments are carried out. Based on collected mean opinion score (MOS) data, we propose a QoE model for point cloud video streaming. We also verify the model by actual subjective scoring, and the results show that the proposed QoE model can accurately reflect users' visual perception. We also make the experimental database public to promote the QoE research of point cloud video streaming.