The Konstanz natural video database (KoNViD-1k)

Vlad Hosu, Franz Hahn, Mohsen Jenadeleh, Hanhe Lin, Hui Men, T. Szirányi, Shujun Li, Dietmar Saupe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

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 number 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.

Original languageEnglish
Title of host publication2017 9th International Conference on Quality of Multimedia Experience, QoMEX 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538640241
DOIs
Publication statusPublished - Jun 30 2017
Event9th International Conference on Quality of Multimedia Experience, QoMEX 2017 - Erfurt, Germany
Duration: May 29 2017Jun 2 2017

Other

Other9th International Conference on Quality of Multimedia Experience, QoMEX 2017
CountryGermany
CityErfurt
Period5/29/176/2/17

Fingerprint

Semantics
Deep learning

Keywords

  • authentic video
  • crowdsourcing
  • fair sampling
  • Video database
  • video quality assessment

ASJC Scopus subject areas

  • Media Technology
  • Human-Computer Interaction
  • Safety, Risk, Reliability and Quality

Cite this

Hosu, V., Hahn, F., Jenadeleh, M., Lin, H., Men, H., Szirányi, T., ... Saupe, D. (2017). The Konstanz natural video database (KoNViD-1k). In 2017 9th International Conference on Quality of Multimedia Experience, QoMEX 2017 [7965673] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/QoMEX.2017.7965673

The Konstanz natural video database (KoNViD-1k). / Hosu, Vlad; Hahn, Franz; Jenadeleh, Mohsen; Lin, Hanhe; Men, Hui; Szirányi, T.; Li, Shujun; Saupe, Dietmar.

2017 9th International Conference on Quality of Multimedia Experience, QoMEX 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7965673.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hosu, V, Hahn, F, Jenadeleh, M, Lin, H, Men, H, Szirányi, T, Li, S & Saupe, D 2017, The Konstanz natural video database (KoNViD-1k). in 2017 9th International Conference on Quality of Multimedia Experience, QoMEX 2017., 7965673, Institute of Electrical and Electronics Engineers Inc., 9th International Conference on Quality of Multimedia Experience, QoMEX 2017, Erfurt, Germany, 5/29/17. https://doi.org/10.1109/QoMEX.2017.7965673
Hosu V, Hahn F, Jenadeleh M, Lin H, Men H, Szirányi T et al. The Konstanz natural video database (KoNViD-1k). In 2017 9th International Conference on Quality of Multimedia Experience, QoMEX 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 7965673 https://doi.org/10.1109/QoMEX.2017.7965673
Hosu, Vlad ; Hahn, Franz ; Jenadeleh, Mohsen ; Lin, Hanhe ; Men, Hui ; Szirányi, T. ; Li, Shujun ; Saupe, Dietmar. / The Konstanz natural video database (KoNViD-1k). 2017 9th International Conference on Quality of Multimedia Experience, QoMEX 2017. Institute of Electrical and Electronics Engineers Inc., 2017.
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