KonIQ-10k IQA Database

Abstract

KonIQ-10k is, at the time of publication, the largest IQA dataset to date consisting of 10,073 quality scored images. This is the first in-the-wild database aiming for ecological validity, with regard to 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 introduce a novel, deep learning model (KonCept512), to show an excellent generalization beyond the test set (0.921 SROCC), to the current state-of-the-art database LIVE-in-the-Wild (0.825 SROCC). The model derives its core performance from the InceptionResNet architecture, being trained at a higher resolution than previous models (512x384). A correlation analysis shows that KonCept512 performs similar to having 9 subjective scores for each test image. The two zip files contain the same images, once in the original resolution of 1024x768px that was used in the study and once downscaled to 512x384px. Mean opinion scores (MOS) as well as the raw distributions acquired through the absolute category ratings are contained in the koniq10k_scores_and_distributions.tab. We additionally provide the auxiliary indicators discussed in the paper in the koniq10k_indicators.tab.