The Konstanz natural video database (KoNViD-1k)

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HOSU, 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. Jun 2017. In: 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX). International Conference on Quality of Multimedia Experience (QoMEX 2017). Erfurt, 31. Mai 2017 - 2. Jun 2017. Piscataway, NJ:IEEE. ISBN 978-1-5386-4024-1

@inproceedings{Hosu2017Konst-39103, title={The Konstanz natural video database (KoNViD-1k)}, year={2017}, doi={10.1109/QoMEX.2017.7965673}, isbn={978-1-5386-4024-1}, address={Piscataway, NJ}, publisher={IEEE}, 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} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/39103"> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-06-01T09:41:07Z</dc:date> <dc:contributor>Hahn, Franz</dc:contributor> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/39103"/> <dc:creator>Lin, Hanhe</dc:creator> <dc:contributor>Lin, Hanhe</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-06-01T09:41:07Z</dcterms:available> <dc:creator>Sziranyi, Tamas</dc:creator> <dc:language>eng</dc:language> <dc:creator>Men, Hui</dc:creator> <dc:contributor>Saupe, Dietmar</dc:contributor> <dc:contributor>Jenadeleh, Mohsen</dc:contributor> <dc:contributor>Men, Hui</dc:contributor> <dcterms:issued>2017</dcterms:issued> <dc:creator>Hosu, Vlad</dc:creator> <dc:creator>Jenadeleh, Mohsen</dc:creator> <dcterms:abstract xml:lang="eng">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.</dcterms:abstract> <dc:contributor>Li, Shujun</dc:contributor> <dc:contributor>Sziranyi, Tamas</dc:contributor> <dcterms:title>The Konstanz natural video database (KoNViD-1k)</dcterms:title> <dc:contributor>Hosu, Vlad</dc:contributor> <dc:creator>Hahn, Franz</dc:creator> <dc:creator>Saupe, Dietmar</dc:creator> <dc:creator>Li, Shujun</dc:creator> </rdf:Description> </rdf:RDF>

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