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Quality assessment of adaptive bitrate videos using image metrics and machine learning
DTU Technical University of Denmark, Denmark.
DTU Technical University of Denmark, Denmark.
RISE, Swedish ICT, Acreo. Mid Sweden University, Sweden.ORCID iD: 0000-0001-5060-9402
2015 (English)In: 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX), 2015, article id 7148105Conference 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.

Place, publisher, year, edition, pages
2015. article id 7148105
Keywords [en]
Artificial intelligence, Machinery, Multimedia systems, Bit rates, Bit-rate video, Cross validation, Hard problems, Image metrics, Machine learning methods, Quality assessment, Spearman rank, Learning systems
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-35468DOI: 10.1109/QoMEX.2015.7148105Scopus ID: 2-s2.0-84939548648ISBN: 978-1-4799-8958-4 (electronic)OAI: oai:DiVA.org:ri-35468DiVA, id: diva2:1259135
Conference
7th International Workshop on Quality of Multimedia Experience (QoMEX 2015), May 26-29, 2015, Pylos-Nestoras, Greece
Available from: 2018-10-27 Created: 2018-10-27 Last updated: 2019-07-12Bibliographically approved

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Brunnstrom, Kjell

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