构建了人类对图像仿射畸变的感知数据集，用于更真实地评估图像质量。
RAID-Database: human Responses to Affine Image Distortions
- 用心理物理学方法测量人眼对旋转、平移、缩放等畸变的主观感受。
- 收集105人对864张失真图像的超过2万次四图比较，结果符合经典感知规律。
- 数据集适合图像质量评估模型训练，尤其关注自然场景中的畸变感知。
图像质量数据库常用于训练预测人类主观感知的模型，但多数现有数据库仅关注数字媒体中的常见失真，而忽略了自然环境中的畸变。仿射变换（如旋转、平移、缩放）是日常生活中最常见的视觉畸变之一，值得专门研究。本文介绍一个包含人类对超阈值仿射变换（旋转、平移、缩放）及高斯噪声响应的数据集，作为与已有图像质量数据库对比的参考。响应通过成熟的心理物理学方法——最大似然差异标度法测量。数据集包含864张失真图像，实验涉及105名观察者，完成超过20000次四图比较。数据质量经验证：(a) 符合经典的Piéron定律；(b) 复现了经典绝对检测阈值；(c) 与传统数据库一致，且在Group-MAD实验中表现更优。
原文摘要 · Abstract (English)
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 Piéron'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.
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