Our purpose is to develop and validate a new iPad-based contrast sensitivity (CS) test for measuring the contrast sensitivity function at near vision (Optopad-CSF). A total of 200 eyes of 100 healthy subjects (ages 17–63) were evaluated in a comparative study between the Optopad-CSF test (near vision) and the CSV-1000E test (distance vision). The agreement between tests was assessed with the index of contrast sensitivity (ICS) and the area under the curve (AUC). CS for all the spatial frequencies in both eyes showed a negative significant correlation with age, and corrected distance, and near visual acuities (r ≤ −0.512, p ≤ 0.013). A significantly lower CS was found with the Optopad-CSF test in the over-40-year-old subgroup for all the spatial frequencies evaluated compared to the below-40 subgroup (p ≤ 0.008). The mean AUC of the Optopad-CSF test (5.84) was twice that of the CSV-1000E test (2.76). The mean ICS of the Optopad-CSF (−0.019) and CSV-1000E (−0.075) tests showed similar values, both close to 0 (p = 0.3). There was a weak but significant correlation between the Optopad-CSF and CSV-1000E ICS tests (r = 0.246, p < 0.02). A range of normality for the values obtained with the Optopad-CSF test was calculated. The mean CS values in 16 bilateral cataract patients were out of the normal range for all the spatial frequencies evaluated (p < 0.001). Optopad-CSF is a valid portable system for measuring CS at near vision for five spatial frequencies, allowing the detection of age-related changes in CSF with age and CSF loss in cataracts, with no ceiling effect.

Image quality datasets are used to train and evaluate predictive models of subjective human perception. However, most existing datasets focus on distortions commonly found in digital media and not in natural conditions. Affine transformations are particularly relevant for 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 transformations (rotation, translation, scaling) and Gaussian noise as convenient reference to compare with existing image quality datasets. The responses were measured using well-established psychophysics: the Maximum Likelihood Difference Scaling (MLDS). The set contains responses to 864 distorted images. The experiments involved 210 observers and over 40,000 image quadruple comparisons. The dataset is validated by two facts: (a) the responses reproduce classical absolute detection thresholds of the affine and Gaussian distortions, and (b) the responses to Gaussian distortion are correlated to the Mean Opinion Score (MOS) of conventional image quality databases for that distortion. Moreover, the classical Piéron’s law applies to the reaction times of the dataset, and Group-MAD adversarial stimuli reveal that MLDS perceptual scales are more accurate than the conventional MOS.