KoNVid-1k
​Natural video benchmark database

A subjectively annotated VQA database consisting of 1,200 public-domain video sequences, fairly sampled from a large public video dataset, YFCC100m, aimed at `in the wild' authentic distortions, depicting a wide variety of content.
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KonIQ-10k
Ecologically valid image benchmark database

A subjectively annotated and ecologically valid IQA database consisting of 10,073 images, on which we performed very large scale crowdsourcing experiments in order to obtain reliable quality ratings from 1467 crowd workers (1.2 million ratings).
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​KADID-10k
Artificially distorted ​image database

The Konstanz Artificially Distorted Image quality Database (KADID-10k), and the Konstanz Artificially Distorted Image quality Set (KADIS-700k). KADID-10k contains 81 pristine images, each degraded by one of 25 distortions, 5 levels each. Each image is scored by 30 degradation category ratings (DCRs). KADIS-700k, has 140,000 pristine images, with 5 randomly degradations each.
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KonPatch-30k
Patch-wise image quality assessment 

Image quality assessment has been studied almost exclusively as a global image property. We extend the notion of quality to spatially restricted sub-regions of images, by individually annotating image patches: 32,000 patches with 10 votes each.
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IQA-Experts-300
Expert annotated ​image benchmark database

Image database consisting of 300 images (200 natural + 100 artificially distorted) scored by 19 freelance experts (professional photographers). Includes information about quality scores as well as presence and types of image degradation.
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​KoSMo-1k
Slow-motion video database

The Konstanz Slow-motion Video Database (KoSMo-1k) consists of 1,350 interpolated video sequences, from 30 different content sources, along with their subjective quality ratings from up to ten subjective comparisons per video pair.
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StudyMB 2.0
Subjective IQA of ​the interpolated images from Middlebury

StudyMB 2.0 consists of 8 sets of artifact-amplified images originate from Middlebury interpolation benchmark. Subjective quality scores of these images were collected from 20 ratings via paired comparisons.
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Picture-wise JND data
Subjective assessment of global picture-wise just noticeable difference

To estimate the picture-wise just noticeable difference (PJND) efficiently, two subjective assessment methods, a slider-based method and a keystroke-based method, were introduced and compared with a traditional one, the relaxed binary search method. The flicker test was applied and the PJND for 10 selected reference images from the MCL-JCI dataset with different distortion levels were assessed. A crowdsourcing study with side-by-side comparisons and forced choice was conducted as well.
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