Color Difference

Accurately measuring color difference (CD) is crucial for various applications, from image processing to digital design, but existing methods often fail to capture human perception, particularly in complex photographic images. Current research focuses on developing CD metrics that better align with human visual experience by incorporating multi-scale analysis, leveraging deep learning architectures like autoregressive normalizing flows, and utilizing large-scale datasets of smartphone photographs to train and evaluate these models. These advancements aim to improve the accuracy and robustness of CD assessment, leading to more effective tools in fields like image editing, quality control, and color reproduction.