TY - GEN
T1 - The Konstanz natural video database (KoNViD-1k)
AU - Hosu, Vlad
AU - Hahn, Franz
AU - Jenadeleh, Mohsen
AU - Lin, Hanhe
AU - Men, Hui
AU - Szirányi, Tamás
AU - Li, Shujun
AU - Saupe, Dietmar
N1 - We thank the German Research Foundation (DFG) for financial support within project A05 of SFB/Transregio 161.
© 2017 IEEE.
PY - 2017/6/30
Y1 - 2017/6/30
N2 - 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.
AB - 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.
KW - authentic video
KW - crowdsourcing
KW - fair sampling
KW - Video database
KW - video quality assessment
UR - http://www.scopus.com/inward/record.url?scp=85026755425&partnerID=8YFLogxK
U2 - 10.1109/QoMEX.2017.7965673
DO - 10.1109/QoMEX.2017.7965673
M3 - Conference contribution
AN - SCOPUS:85026755425
SN - 978-1-5386-4025-8
T3 - 2017 9th International Conference on Quality of Multimedia Experience, QoMEX 2017
BT - 2017 9th International Conference on Quality of Multimedia Experience (QoMEX 2017)
PB - IEEE
T2 - 9th International Conference on Quality of Multimedia Experience, QoMEX 2017
Y2 - 29 May 2017 through 2 June 2017
ER -