Blue Shift Assumption: Improving Illumination Estimation Accuracy for Single Image from Unknown Source

Color constancy methods for removing the influence of illumination on object colors are divided into statistics-based and learning-based ones. The latter have low illumination estimation error, but only on images taken with the same sensor and in similar conditions as the ones used during training. For an image taken with an unknown sensor, a statistics-based method will often give higher accuracy than an untrained or specifically trained learning-based method because of its simpler assumptions not bounded to any specific sensor. The accuracy of a statistics-based method also depends on its parameter values, but for an image from an unknown source these values can be tuned only blindly. In this paper the blue shift assumption is proposed, which acts as a heuristic for choosing the optimal parameter values in such cases. It is based on real-world illumination statistics coupled with the results of a subjective user study and its application outperforms blind tuning in terms of accuracy. The source code is available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.