Rank-Based Illumination Estimation

A new two-stage illumination estimation method based on the concept of rank is presented. The method first estimates the illuminant locally in subwindows using a ranking of digital counts in each color channel and then combines local subwindow estimates again based on a ranking of the local estimates. The proposed method unifies the MaxRGB and Grayworld methods. Despite its simplicity, the performance of the method is found to be competitive with other state-of-the art methods for estimating the chromaticity of the overall scene illumination.

[1]  Ben Weiss.  Fast median and bilateral filtering , 2006, SIGGRAPH 2006.

[2]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[3]  B A Wandell,et al.  Scene illuminant classification: brighter is better. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[4]  Andrew Blake,et al.  Bayesian color constancy revisited , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Byoung-Ho Kang,et al.  Illumination Estimation via Thin-Plate Spline Interpolation , 2007, CIC.

[6]  Brian V. Funt,et al.  A comparison of computational color constancy Algorithms. II. Experiments with image data , 2002, IEEE Trans. Image Process..

[7]  Joost van de Weijer,et al.  Author Manuscript, Published in "ieee Transactions on Image Processing Edge-based Color Constancy , 2022 .

[8]  Brian V. Funt,et al.  A Large Image Database for Color Constancy Research , 2003, CIC.

[9]  Kobus Barnard,et al.  Estimating the scene illumination chromaticity by using a neural network. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[10]  Graham D. Finlayson,et al.  Color by Correlation: A Simple, Unifying Framework for Color Constancy , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Mark S. Drew,et al.  The Role of Bright Pixels in Illumination Estimation , 2012, Color Imaging Conference.

[12]  G. Buchsbaum.  A spatial processor model for object colour perception , 1980 .

[13]  B. Funt.  MaxRGB Reconsidered , 2012 .

[14]  Joost van de Weijer,et al.  Generalized Gamut Mapping using Image Derivative Structures for Color Constancy , 2008, International Journal of Computer Vision.

[15]  Keigo Hirakawa,et al.  Color Constancy with Spatio-Spectral Statistics , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Graham D. Finlayson,et al.  Shades of Gray and Colour Constancy , 2004, CIC.