Semantic-Driven Selection of Printer Color Rendering Intents

In this paper we introduce a unified framework that automatically selects the optimal color rendering intent for a given print job. We first present how we extract information from both the image features and the semantic information contained in keywords attached to this image. Then we show how our framework unifies the two inputs to select the optimal ICC rendering intent. The framework is evaluated with a psychophysical experiment on an image data set printed with the ICC media-relative colorimetric and perceptual intents using an Océ large format printer. We find that our method is correctly able to predict the observers preferences in 81% of the images tested when the keyword is included compared to 58% when the keyword is not included.