# RAID-Database: human Responses to Affine Image Distortions

arXiv: https://arxiv.org/abs/2412.10211 · HTML: https://mlsanity.com/paper/2412.10211/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration](https://mlsanity.com/paper/2007.12142/) (2020)
- [PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance](https://mlsanity.com/paper/1910.12548/) (2019)
- [Assessing invariance to affine transformations in image quality metrics](https://mlsanity.com/paper/2407.17927/) (2024)

## Built on by (most cited first, 1 of 1)

- [Parameter-Efficient Architectural Modifications for Translation-Invariant CNNs](https://mlsanity.com/paper/2604.27870/) (2026)

## Abstract (arXiv)

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.