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We present a novel framework for automatically determining whether or not to apply black point compensation (BPC) in image reproduction. Visually salient objects have a larger influence on determining image quality than the number of dark pixels in an image, and thus should drive the use of BPC. We propose a simple and efficient algorithmic implementation to determine when to apply BPC based on low-level saliency estimation. We evaluate our algorithm with a psychophysical experiment on an image data set printed with or without BPC on a Canon printer. We find that our algorithm is correctly able to predict the observers’ preferences in all cases when the saliency maps are unambiguous and accurate.