MaxRGB Reconsidered

The poor performance of the MaxRGB illuminationestimation 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 whiteequivalent 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 miniColorcheckers placed at an angle to one another. MaxRGB is found to perform surprisingly well when tested on either the multipleexposure 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 EdgeBased 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. VC 2012 Society for Imaging Science and Technology. [DOI: 10.2352/J.ImagingSci. Technol.2012. 56.2. 020501] INTRODUCTION MaxRGB is a very simple method of estimating the chromaticity (i.e., [r, g, b]1⁄4 [R=(RþGþB), G=(RþGþB), B=(RþGþB)]) of the scene illumination for color constancy and automatic white balancing based on the assumption that the triple of the maximal values obtained independently from each of the three color channels represents the color of the illumination. It has been much maligned in the literature; however, is its performance really as bad as it has been reported to be? MaxRGB is a special and extremely limited case of retinex. In particular, it corresponds to McCann99 Retinex when the number of iterations is infinite, or to path-based retinex without thresholding but with infinite paths. Retinex and MaxRGB both depend on the assumption that one of the following holds as: (1) there is a white surface in the scene, (2) there is a shiny surface in the scene creating a specular highlight having the same chromaticity as the incident light, and (3) there are one or more separate surfaces reflecting maximally in the R, G, and B sensitivity ranges. Condition (3) is similar to condition (1) except that for condition (1), the three maxima occur at a single spatial location because an ideal white surface reflects maximally across all three sensitivity ranges. For condition (3), the maximal reflectances for the different color channels are allowed to come from multiple locations. MaxRGB also depends on the assumption, common to many illumination-estimation methods, that the chromaticity of the illumination is constant throughout the scene. MaxRGB estimates the chromaticity of the scene illumination as the chromaticity of the triplet (maximal R, maximal G, and maximal B). In practice, most digital still cameras are incapable of capturing the full dynamic range of a scene and use exposures and tone reproduction curves that clip or compress high values. As a result, the maximum R, G, and B digital counts from an image generally do not faithfully represent the corresponding maximum scene radiances. Funt et al. present some tests using artificial clipping of images that show the effect that lack of dynamic range can have on various illumination-estimation algorithms. We hypothesize that there are two reasons why the effectiveness of MaxRGB may have been underestimated. One is that it is important not only to apply MaxRGB as the simple maximum of each channel but rather it is necessary to preprocess the image data somewhat before calculating the maximum; otherwise, a single bad pixel or spurious noise will lead to the maximum being incorrect. The second, and perhaps more important reason, is that MaxRGB generally has been applied to 8-bit-per-channel, nonlinear images, for which there is likely to be significant tone-curve compression as well as clipping of high values, both of which affect the chromaticity of the maximal values of R, G, and B. To test the first hypothesis, the effects on performance of three different preprocessing strategies are tested. To test the second hypothesis, a new dataset for color constancy research has been constructed that consists of images of ~ IS&T Member. Received May 23, 2011; accepted for publication Apr. 4, 2012; published online May 22, 2012. 1062-3701/2012/56(2)/020501/10/$20.00. J. Imaging Sci. Technol. Mar.-Apr. 2012 020501-1 Journal of Imaging Science and TechnologyV 56(2): 020501-1–020501-10, 2012. VC Society for Imaging Science and Technology 2012