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Content-based user viewport prediction for live virtual reality streaming
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Feng, Xianglong. Content-based user viewport prediction for live virtual reality streaming. Retrieved from https://doi.org/doi:10.7282/t3-m1z4-3841

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TitleContent-based user viewport prediction for live virtual reality streaming
NameFeng, Xianglong (author); Wei, Sheng (chair); Chen, Yingying (member); Yuan, Bo (member); Liu, Yao (member); Rutgers University; School of Graduate Studies
Date Created2021
Other Date2021-10 (degree)
SubjectComputer engineering, Computer vision, Viewport prediction, Virtual reality
Extent1 online resource (x, 69 pages) : illustrations
DescriptionVirtual reality (VR) streaming (a.k.a., 360-degree video streaming) has been gaining popularity recently as a new form of multimedia providing the users with immersive viewing experience. However, the high volume of data for the 360-degree video frames creates significant bandwidth challenges. Research efforts have been made to reduce the bandwidth consumption by predicting and selectively streaming the user's viewports. However, the existing viewport prediction approaches require historical user or video data and cannot be applied to live streaming, the most attractive VR streaming scenario. We develop a set of viewport prediction methods targeting live VR streaming by employing various features obtained from the video content, such as motion (i.e., LiveMotion), deep features (i.e., LiveDeep), object semantics (i.e., LiveObj), and action semantics (i.e., LiveROI). Furthermore, we enhance the video content-based prediction by leveraging runtime user feedback and modeling. Our evaluations using an empirical VR head movement dataset demonstrate high prediction accuracy and significant bandwidth savings obtained by the four methods. Besides, they achieve real-time performance with low processing delay, meeting the requirement of live VR streaming.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
Persistent URLhttps://doi.org/doi:10.7282/t3-m1z4-3841
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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