KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment

Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content and annotating it accurately. We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dataset to date, consisting of 10,073 quality scored images. It is the first in-the-wild database aiming for ecological validity, concerning the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models. We propose a…

• DF
  Deutsche Forschungsgemeinschaft

  Award: 251654672 – TRR 161 (Project A05)
• HS
  Hungarian Scientific Research Fund

  Award: OTKA 120499