Analysis of the Impact of RGB-to-Achromatic Color Space Transformations on Single-Image Superresolution Performance

Super-resolution techniques are employed to enhance the quality of digital images. Color spaces are developed to model colors in various digital environments. In the literature, several studies suggest that applying color space transformations and subsequently employing super-resolution techniques on the transformed images improve image quality. This study analyzes the impact of color space trans-formations on super-resolution applications. The analysis is conducted by performing the super-resolution process entirely in the RGB color space, followed by converting the obtained result into a different color space and comparing the quality metrics. The findings reveal that it is possible to achieve higher scores by converting RGB images into YCbCr or CIELab color spaces, despite no actual improvement in perceived image quality. Our experiments involve applying image enhancement techniques solely within the RGB color space, converting the results into alternative color spaces, and comparing them with ground truth images in Set5, Set14, BSDS100, Urban100, and DIV2K. Working in color spaces other than RGB does not lead to significant visual quality improvement. Our experiments demonstrate that solely through color space conversion, traditional metrics such as PSNR and SSIM, as well as deep learning-based metrics like DISTS and A-DISTS, can yield higher scores. Therefore, the observed improvements in quality metrics resulting from color space transformations may be misleading and may not reflect actual enhancements in image fidelity. With the A-DISTS metric that evaluates human perception, our study examines not only the impact of transformations from RGB to alternative color spaces on metrics but also evaluates the alignment of these metrics with human perception, an area that has received limited attention in the literature.