An Image Database for Design and Evaluation of Visual Quality Metrics in Synthetic Scenarios

This paper presents a new image database which provides images for evaluation and design of visual quality assessment metrics. It contains 1688 images, 8 reference images, 7 types of distortions per reference image and 30 distortions per type and reference. The distortion types address image errors arising in visual compositions of real and synthetic content, thus provide a basis for visual quality assessment metrics targeting augmented and virtual reality content. In roughly 200 subjective experiments over 17.000 evaluations have been gathered and Mean Opinion Scores for the database have been obtained. The evaluation of several existing and widely used quality metrics on the proposed database is included in this paper. The database is freely available, reproducible and extendable for further scientific research.