Evaluating Color Spaces for Evolutionary Image Contrast Enhancement: An Empirical Study

Image processing is a fundamental field in computer science with applications across various real-life areas. Image enhancement in the preprocessing stage is crucial for tasks in computer vision. Contrast enhancement in images aims to improve visual quality by increasing contrast and highlighting significant details. Although classical contrast enhancement techniques are widely used, they often suffer from issues such as over-enhancement due to the lack of mechanisms to control this improvement. To improve the contrast, transformation functions assign new intensities to each pixel in the image. One of the main drawbacks of the transformation functions is tuning their parameters. On the other hand, most contrast-enhancement techniques are typically used to improve the contrast in color images. In this regard, this study examines the effectiveness of three color spaces-HSV, HSI, and CIELAB-in enhancing contrast, with the goal of identifying the most effective space for this purpose. Additionally, the performance of a widely used metaheuristic is evaluated in tuning the parameters of the transformation function. The model is evaluated using standard quality indicators on a public image dataset. Preliminary findings suggest that the HSI color space is better suited for optimization using metaheuristics and effectively improves image contrast.