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Quality assessment of adaptive bitrate videos using image metrics and machine learning
Technical University of Denmark, Kgs Lyngby, Denmark.
Technical University of Denmark, Kgs Lyngby, Denmark.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information and Communication systems. Acreo Swedish ICT AB, Kista, Sweden.ORCID iD: 0000-0001-5060-9402
2015 (English)In: 2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015, 2015, Art. no. 7148105- p.Conference paper, Published paper (Refereed)
Abstract [en]

Adaptive bitrate (ABR) streaming is widely used for distribution of videos over the internet. In this work, we investigate how well we can predict the quality of such videos using well-known image metrics, information about the bitrate levels, and a relatively simple machine learning method. Quality assessment of ABR videos is a hard problem, but our initial results are promising. We obtain a Spearman rank order correlation of 0.88 using content-independent cross-validation. © 2015 IEEE.

Place, publisher, year, edition, pages
2015. Art. no. 7148105- p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-25919DOI: 10.1109/QoMEX.2015.7148105ISI: 000375091800026Scopus ID: 2-s2.0-84939548648Local ID: STCISBN: 9781479989584 (print)OAI: oai:DiVA.org:miun-25919DiVA: diva2:856245
Conference
2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015, 26 May 2015 through 29 May 2015
Note

Export Date: 23 September 2015

Available from: 2015-09-23 Created: 2015-09-23 Last updated: 2016-12-23Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
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  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
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More languages
Output format
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  • asciidoc
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