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Objective Video Quality Assessment - Towards large scale video database enhanced model development
University of Nantes, France.
Polytechnic University of Turin, Italy.
Ghent University, Belgium.
RISE., Swedish ICT, Acreo. Mid Sweden University, Sweden.
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2015 (Engelska)Ingår i: IEICE transactions on communications, ISSN 0916-8516, E-ISSN 1745-1345, Vol. E98.B, nr 1, s. 2-11Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The current development of video quality assessment algorithms suffers from the lack of available video sequences for training, verification and validation to determine and enhance the algorithm’s application scope. The Joint Effort Group of the Video Quality Experts Group (VQEG-JEG) is currently driving efforts towards the creation of large scale, reproducible, and easy to use databases. These databases will contain bitstreams of recent video encoders (H.264, H.265), packet loss impairment patterns and impaired bitstreams, pre-parsed bitstream information into files in XML syntax, and well-known objective video quality measurement outputs. The database is continuously updated and enlarged using reproducible processing chains. Currently, more than 70,000 sequences are available for statistical analysis of video quality measurement algorithms. New research questions are posed as the database is designed to verify and validate models on a very large scale, testing and validating various scopes of applications, while subjective assessment has to be limited to a comparably small subset of the database. Special focus is given on the principles guiding the database development, and some results are given to illustrate the practical usefulness of such a database with respect to the detailed new research questions.

Ort, förlag, år, upplaga, sidor
2015. Vol. E98.B, nr 1, s. 2-11
Nyckelord [en]
video quality assessment, large scale database, reproducible research
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:ri:diva-31917DOI: 10.1587/transcom.E98.B.2OAI: oai:DiVA.org:ri-31917DiVA, id: diva2:1151775
Tillgänglig från: 2017-10-24 Skapad: 2017-10-24 Senast uppdaterad: 2019-07-05Bibliografiskt granskad

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