OLIMP

The dataset for color vision deficiency (CVD) and refractive error (RE) precompensation tasks includes 1320 images organized into 11 main categories, each containing 6 subcategories of 20 images each. The main goal of this dataset is to provide a variety of visual data that can be used for analyzing and developing methods for precompensating visual distortions.

The image categories cover various aspects of visual perception, including nature, urban scenes, interiors, portraits, animals, food and drink, art and culture, sports, objects and subjects, visual abstractions, as well as text and icons. Such a wide range of categories provides comprehensive coverage of possible scenarios that a person with visual impairments may encounter.

The images in the dataset are carefully selected to demonstrate maximum diversity in contrast, lighting, and color palettes, which is especially important for tasks related to CVD and RE precompensation. The dataset was compiled from freely available images from various online resources, and each image is accompanied by a link to the source and information about the license type.

This dataset can be used by researchers and developers in the field of computer vision and ophthalmology to create and test algorithms aimed at improving the perception of visual information by people with visual impairments.