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Sca-2023: a two-part dataset for benchmarking the methods of image precompensation for users with refractive errors

• Nafe B. Alkzir
• Ilia P. Nikolaev
• Dmitry P. Nikolaev

(2023). ECMS 2023, 37th Proceedings
Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation.
DOI: http://doi.org/10.7148/2023
ISSN: 2522-2422 (ONLINE)
ISSN: 2522-2414 (PRINT)
ISSN: 2522-2430 (CD-ROM)
ISBN: 978-3-937436-80-7
ISBN: 978-3-937436-79-1 (CD) Communications of the ECMS Volume 37, Issue 1, June 2023, Florence, Italy June 20th – June 23rd, 2023

https://doi.org/10.7148/2023-0298

Nafe b. alkzir, Ilia p. nikolaev, Dmitry p. nikolaev (2023). SCA-2023: A two-part dataset for benchmarking the methods of image precompensation for users with refractive errors, ECMS 2023, Proceedings Edited by: Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni, European Council for Modelling and Simulation. doi:10.7148/2023-0298

This paper considers the problem 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 to for implementing the precompensation transform, but also a separate set of images characterizing specific types and degrees of manifestation of the refractive errors. The second part of the dataset is used for computer simulation of the so-called retinal image (the distribution of light on the retina of an imaginary observer). We demonstrated the capabilities of our approach using three prior-art precompensation methods and found that not all the image comparison metrics provide adequate results when applied to precompensated retinal images.