A Comparison of Computational Color Constancy Algorithms; Part Two: Experiments with Image Data

We test a number of the leading computational color constancy algorithms using a comprehensive set of images. These were of 33 different scenes under 11 different sources representative of common illumination conditions. The algorithms studied include two gray world methods, a version of the Retinex method, several variants of Forsyth's gamut-mapping method, Cardei et al.'s neural net method, and Finlayson et al.'s Color by Correlation method. We discuss a number of issues in applying color constancy ideas to image data, and study in depth the effect of different pre-processing strategies. We compare the performance of the algorithms on image data with their performance on synthesized data. All data used for this study is available on-line at http://www.cs.sfu.ca/~colour/data, and implementations for most of the algorithms are also available (http://www.cs.sfu.ca/~colour/code). Experiments with synthesized data (part one of this paper) suggested that the methods which emphasize the use of the input data statistics, specifically Color by Correlation and the neural net algorithm, are potentially the most effective at estimating the chromaticity of the scene illuminant. Unfortunately, we were unable to realize comparable performance on real images. Here exploiting pixel intensity proved to be more beneficial than exploiting the details of image chromaticity statistics, and the three-dimensional gamut-mapping algorithms gave the best performance.