The poor performance of the MaxRGB illumination-estimation method is often used in the literature as a foil when promoting some new illumination-estimation method. However, MaxRGB has usually been tested on images of only 8-bits per channel, where clipping of high radiances is likely
to have occurred. The question arises as to whether the method itself is inadequate, or rather whether it has simply been tested on data of inadequate dynamic range or with inadequate preprocessing. In particular, is MaxRGB's underlying assumption that there is a white
or white-equivalent surface present in every scene too strong? This question is explored here in two ways. The first avenue of investigation is based on a new database of 105 sets of multiple-exposure images. High-dynamic range images are constructed from these sets as well. The color of the
scene illumination is determined by taking an extra image of the scene containing four Gretag Macbeth mini-Colorcheckers placed at an angle to one another. MaxRGB is found to perform surprisingly well when tested on either the multiple-exposure or the high-dynamic range images. The second
avenue of investigation is to add some simple preprocessing to the basic MaxRGB algorithm. By removing clipped pixels followed by median filtering, MaxRGB also performs better than previously reported when tested on test images of common color constancy test sets, specifically the Simon Fraser
University 321-image indoor set. In particular, the Wilcoxon signed-rank test indicates that MaxRGB outperforms the most recent bright-pixel variant of color by correlation on the 321 set. MaxRGB is also competitive against the recent Edge-Based algorithm and significantly better than the
computationally intensive Bayesian method on the Grayball set and the Colorchecker set. Overall, the results presented demonstrate that MaxRGB is far more effective than it has been reputed to be.
School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada
Publication date: March 1, 2012
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The Journal of Imaging Science and Technology (JIST) is dedicated to the advancement of imaging science knowledge, the practical applications of such knowledge, and how imaging science relates to other fields of study. The pages of this journal are open to reports of new theoretical or experimental results, and to comprehensive reviews. Only original manuscripts that have not been previously published, nor currently submitted for publication elsewhere, should be submitted.