A neural classifier to evaluate the role of image resolution in the perception of color differences

Measuring the color difference between an image and a copy is crucial in the color industry, as many printing processes exist whose aim is to accurately reproduce colors. Due to various phenomena that are often unpredictable, these processes may print copies that are perceptually different from the original. Visual inspections are thus required in order to constantly control the color quality. These inspections are made by experts who observe a master image and a copy, typically at high resolution. This takes a long time for image acquisition, and generates high costs that could be reduced if these processes could be performed at lower resolution, possibly without human intervention, as people may perceive colors in different ways. This paper presents a neural network that assesses the color difference of multicolored images (photos) at different resolutions. The results showed that the level of color difference perceived by the network remains unchanged as the resolution decreases, and sometimes is even more evident.