@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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    <dc:contributor>Hosu, Vlad</dc:contributor>
    <dcterms:title>The Konstanz natural video database (KoNViD-1k)</dcterms:title>
    <dc:creator>Jenadeleh, Mohsen</dc:creator>
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    <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:creator>Men, Hui</dc:creator>
    <dc:creator>Hahn, Franz</dc:creator>
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    <dc:creator>Saupe, Dietmar</dc:creator>
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