TY - GEN
T1 - KADID-10k
T2 - 11th International Conference on Quality of Multimedia Experience
AU - Lin, Hanhe
AU - Hosu, Vlad
AU - Saupe, Dietmar
N1 - Funding Information:
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Projektnummer 251654672 TRR 161 (Project A05).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6/24
Y1 - 2019/6/24
N2 - Current artificially distorted image quality assessment (IQA) databases are small in size and limited in content. Larger IQA databases that are diverse in content could benefit the development of deep learning for IQA. We create two datasets, the Konstanz Artificially Distorted Image quality Database (KADID-10k) and the Konstanz Artificially Distorted Image quality Set (KADIS-700k). The former contains 81 pristine images, each degraded by 25 distortions in 5 levels. The latter has 140,000 pristine images, with 5 degraded versions each, where the distortions are chosen randomly. We conduct a subjective IQA crowdsourcing study on KADID-10k to yield 30 degradation category ratings (DCRs) per image. We believe that the annotated set KADID-10k, together with the unlabelled set KADIS-700k, can enable the full potential of deep learning based IQA methods by means of weakly-supervised learning.
AB - Current artificially distorted image quality assessment (IQA) databases are small in size and limited in content. Larger IQA databases that are diverse in content could benefit the development of deep learning for IQA. We create two datasets, the Konstanz Artificially Distorted Image quality Database (KADID-10k) and the Konstanz Artificially Distorted Image quality Set (KADIS-700k). The former contains 81 pristine images, each degraded by 25 distortions in 5 levels. The latter has 140,000 pristine images, with 5 degraded versions each, where the distortions are chosen randomly. We conduct a subjective IQA crowdsourcing study on KADID-10k to yield 30 degradation category ratings (DCRs) per image. We believe that the annotated set KADID-10k, together with the unlabelled set KADIS-700k, can enable the full potential of deep learning based IQA methods by means of weakly-supervised learning.
KW - Crowdsourcing
KW - Image quality assessment
KW - Image quality dataset
UR - http://www.scopus.com/inward/record.url?scp=85068701197&partnerID=8YFLogxK
U2 - 10.1109/QoMEX.2019.8743252
DO - 10.1109/QoMEX.2019.8743252
M3 - Conference contribution
AN - SCOPUS:85068701197
SN - 978-1-5386-8213-5
T3 - 2019 11th International Conference on Quality of Multimedia Experience, QoMEX 2019
BT - 2019 11th International Conference on Quality of Multimedia Experience (QoMEX 2019)
PB - IEEE
Y2 - 5 June 2019 through 7 June 2019
ER -