The recent practices of demonstrating various static and video images to users by means of digital, processor-controlled, often self-luminous devices (computer monitors, smartphone and tablet screens, etc.) have spurred the development of various methods for improving the perception of such images through their computer processing. In particular, this applies to the task of precompensating images shown to users with various anomalies of refraction of the eyes (e.g. myopia or astigmatism) in situations where they are not equipped with glasses or other corrective devices. Researchers have proposed a considerable number of such precompensation methods, but to this day there has been no way to accurately compare their quality. We propose an original dataset, which we called “SCA-2023”, of images specially designed for this purpose. Its most important feature is the fact that it includes not only a set of ground-truth images for implementing the precompensation transform, but also a separate set of images characterizing specific types and degrees of manifestation of the refractive errors. The benchmarking procedure itself includes applying the precompensation transformation to a certain image from the first part of the dataset, computer simulation of the so-called retinal image (distribution of light on the retina of an imaginary observer) based on the selection of the “distorting eye” from the second part of the dataset, and evaluating the similarity of this image to the ground-truth image, using any of the commonly used similarity metrics for this purpose.