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Subjective analysis and objective characterization of adaptive bitrate videos
Technical University of Denmark, Lyngby, Denmark.
Universidad Politecnica de Madrid, Madrid, Spain.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems. Acreo Swedish ICT AB.ORCID iD: 0000-0001-5060-9402
Universidad Politecnica de Madrid, Madrid, Spain.
2016 (English)In: Human Vision and Electronic Imaging 2016, HVEI 2016, The Society for Imaging Science and Technology, 2016, 55-63 p.Conference paper, (Refereed)
Abstract [en]

The HTTP Adaptive Streaming (HAS) technology allows video service providers to improve the network utilization and thereby increasing the end-users' Quality of Experience (QoE). This has made HAS a widely used approach for audiovisual delivery There are several previous studies aiming to identify the factors influencing on subjective QoE of adaptation events. However, adapting the video quality typically lasts m a time scale much longer than what current standardized subjective testing methods are designed for. thus making the full matrix design of the experiment on an event level hard to achieve. In this study, we investigated the overall subjective QoE of 6 minutes long video sequences containing different sequential adaptation events. This was compared to a dataset from our previous work performed to evaluate the individual adaptation events. We could then derive a relationship between the overall mean opinion score (MOS) and the MOS from shorter sequences. The aforementioned empirical dataset has proven to be very challenging in terms of video quality assessment test design, thus deriving a conclusive outcome about the influence of different parameters have been difficult. The second contribution of this study is considering how objective characterizations of adapted videos can improve the understanding of the subjective ratings.

Place, publisher, year, edition, pages
The Society for Imaging Science and Technology, 2016. 55-63 p.
National Category
Media Engineering
Identifiers
URN: urn:nbn:se:miun:diva-30124DOI: 10.2352/ISSN.2470-1173.2016.16.HVEI-105Scopus ID: 2-s2.0-85011024568ISBN: 9781510827943 (print)OAI: oai:DiVA.org:miun-30124DiVA: diva2:1074068
Conference
Human Vision and Electronic Imaging 2016, HVEI 2016, 14 February 2016 through 18 February 2016, San Francisco; United States
Note

Conference Paper

Available from: 2017-02-14 Created: 2017-02-14 Last updated: 2017-06-30Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf