DU Lina, ZHUO Li, LI Jiafeng. Survey on QoE Evaluation of Panoramic Video[J]. JOURNAL OF SIGNAL PROCESSING, 2022, 38(9): 1831-1842. DOI: 10.16798/j.issn.1003-0530.2022.09.006
Citation: DU Lina, ZHUO Li, LI Jiafeng. Survey on QoE Evaluation of Panoramic Video[J]. JOURNAL OF SIGNAL PROCESSING, 2022, 38(9): 1831-1842. DOI: 10.16798/j.issn.1003-0530.2022.09.006

Survey on QoE Evaluation of Panoramic Video

  • ‍ ‍With the continuous advancement of the 5th Generation (5G) mobile communication technology, high-performance computing and sensing technologies, panoramic video has attracted more and more research attention. Through head-mounted display devices, panoramic video can provide users with realistic and immersive visual perception far exceeding that of flat video, and has a promising prospect. Providing users with a good Quality of Experience (QoE) is the key for video service providers to attract and retain users and succeed in the fierce market competition. Compared with the usual flat video, the data volume of panoramic video usually increases exponentially, which will put forward higher requirements for the acquisition, coding, transmission and storage of video data. So how to ensure users’ QoE with limited network transmission bandwidth and storage resources has become a hot research topic in both academia and industry recently. In this paper, the QoE evaluation of panoramic video is mainly reviewed. Firstly, the possible distortion types of panoramic video in the process of video acquisition, stitching, projection, encoding, transmission, decoding, back projection, rendering, and so on, are analyzed comprehensively, and a variety of subjective and objective influencing factors of the users’ QoE are summarized, such as human factors, system factors, context, and video content characteristics and so on. On this basis, the research advances of the QoE evaluation model of panoramic video are highlighted from the aspects of influencing factors, modeling methods and so on. And the main applications of the QoE evaluation model of panoramic video in the Adaptive Bit Rate (ABR) algorithm, resource optimization allocation, bit rate control, etc, are analyzed. Finally, several representative QoE evaluation datasets of panoramic video and commonly used performance evaluation metrics are introduced. The existing problems and the future tendency of the QoE evaluation model of panoramic video are also further discussed.
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