RAID-Database: human Responses to Affine Image Distortions

TL;DR

This paper introduces a new image quality database focusing on human responses to affine distortions like rotation, translation, and scaling, providing a valuable resource for understanding perception of natural image distortions.

Contribution

It presents a comprehensive dataset of human perceptual responses to affine image distortions, filling a gap in existing image quality databases and enabling better model training.

Findings

Responses reproduce Piéron's law

Dataset aligns with classical detection thresholds

Improves upon existing image quality databases

Abstract

Image quality databases are used to train models for predicting subjective human perception. However, most existing databases focus on distortions commonly found in digital media and not in natural conditions. Affine transformations are particularly relevant to study, as they are among the most commonly encountered by human observers in everyday life. This Data Descriptor presents a set of human responses to suprathreshold affine image transforms (rotation, translation, scaling) and Gaussian noise as convenient reference to compare with previously existing image quality databases. The responses were measured using well established psychophysics: the Maximum Likelihood Difference Scaling method. The set contains responses to 864 distorted images. The experiments involved 105 observers and more than 20000 comparisons of quadruples of images. The quality of the dataset is ensured because (a) it reproduces the classical Pi\'eron's law, (b) it reproduces classical absolute detection thresholds, and (c) it is consistent with conventional image quality databases but improves them according to Group-MAD experiments.