Publikation:

The Konstanz natural video database (KoNViD-1k)

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Hosu_2-e7mh9z8d8u09.pdf
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Datum

2017

Autor:innen

Sziranyi, Tamas

Herausgeber:innen

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ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
http://nbn-resolving.de/urn:nbn:de:bsz:352-2-e7mh9z8d8u09
DOI (zitierfähiger Link)
https://doi.org/10.1109/QoMEX.2017.7965673
ArXiv-ID

Internationale Patentnummer

Link zur Lizenz
Urheberrechtlich geschützt

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Green

Sammlungen

Informatik und Informationswissenschaft: Publikationen
Core Facility der Universität Konstanz

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Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX). Piscataway, NJ: IEEE, 2017. ISBN 978-1-5386-4024-1. Available under: doi: 10.1109/QoMEX.2017.7965673

Zusammenfassung

Subjective video quality assessment (VQA) strongly depends on semantics, context, and the types of visual distortions. Currently, all existing VQA databases include only a small num- ber of video sequences with artificial distortions. The development and evaluation of objective quality assessment methods would benefit from having larger datasets of real-world video sequences with corresponding subjective mean opinion scores (MOS), in particular for deep learning purposes. In addition, the training and validation of any VQA method intended to be ‘general purpose’ requires a large dataset of video sequences that are representative of the whole spectrum of available video content and all types of distortions. We report our work on KoNViD-1k, a subjectively annotated VQA database consisting of 1,200 public- domain video sequences, fairly sampled from a large public video dataset, YFCC100m. We present the challenges and choices we have made in creating such a database aimed at ‘in the wild’ authentic distortions, depicting a wide variety of content.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Video database, authentic video, video qualitiy assessment, fair samling, crowdsourcing

Konferenz

International Conference on Quality of Multimedia Experience (QoMEX 2017), 31. Mai 2017 - 2. Juni 2017, Erfurt
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ISO 690HOSU, Vlad, Franz HAHN, Mohsen JENADELEH, Hanhe LIN, Hui MEN, Tamas SZIRANYI, Shujun LI, Dietmar SAUPE, 2017. The Konstanz natural video database (KoNViD-1k). International Conference on Quality of Multimedia Experience (QoMEX 2017). Erfurt, 31. Mai 2017 - 2. Juni 2017. In: 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX). Piscataway, NJ: IEEE, 2017. ISBN 978-1-5386-4024-1. Available under: doi: 10.1109/QoMEX.2017.7965673
BibTex
@inproceedings{Hosu2017Konst-39103,
  year={2017},
  doi={10.1109/QoMEX.2017.7965673},
  title={The Konstanz natural video database (KoNViD-1k)},
  url={https://www.uni-konstanz.de/mmsp/pubsys/publishedFiles/HoHaJe17.pdf},
  isbn={978-1-5386-4024-1},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX)},
  author={Hosu, Vlad and Hahn, Franz and Jenadeleh, Mohsen and Lin, Hanhe and Men, Hui and Sziranyi, Tamas and Li, Shujun and Saupe, Dietmar}
}
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Interner Vermerk

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Kontakt
URL der Originalveröffentl.
https://www.uni-konstanz.de/mmsp/pubsys/publishedFiles/HoHaJe17.pdf

Prüfdatum der URL

2017-05-11

Prüfungsdatum der Dissertation

Finanzierungsart

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