Quality of Experience of Viewport Adaptive Omnidirectional Video Streaming

Abstract

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With the explosive growth of multimedia streaming services and virtual reality devices, omnidirectional video (ODV) is becoming increasingly popular in practical applications. However, streaming the entire ODV with high definition and high frame rate induces a waste of bandwidth. The tile-based viewport adaptive streaming provides a solution to overcome volatile network conditions, while the scheme would lead to quality adaptation when the network changes dynamically. In this paper, we focus on investigating how the human visual quality of experience (QoE) changes with time-varying ODV quality. Specifically, we construct a new quality of experience database for viewport adaptive ODV streaming named JUFEOVQoE, which includes twelve original ODVs with diverse content, and corresponding 378 viewport videos generated by compressing the raw viewport videos using a variety of combinations of quantization parameter (QP), spatial (S), and temporal resolutions (T). We conduct a series of subjective experiments to collect the mean opinion scores of the viewport video sequences and the viewing direction data of the subjects. Furthermore, we test several state-of-the-art objective QoE models on the proposed database. Experimental results demonstrate that existing mainstream QoE methods cannot predict the QoE of the viewport adaptive streaming ODVs accurately. The database will be released to facilitate further research.