A comparison of computational color constancy algorithms - Part II: Experiments with image data

Kobus Barnard, Lindsay Martin, Adam Coath, Brian Funt

Research output: Contribution to journalArticlepeer-review

275 Scopus citations


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 preprocessing 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 online at http://www.cs.sfu.ca/~color/data, and implementations for most of the algorithms are also available (http://www.cs.sfu.ca/∼color/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 (3-D) gamut-mapping algorithms gave the best performance.

Original languageEnglish (US)
Pages (from-to)985-996
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number9
StatePublished - Sep 2002


  • Algorithm
  • Color by correlation
  • Color constancy
  • Comparison
  • Computational
  • Gamut constraint
  • Neural network

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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