Cubical Gamut Mapping Colour Constancy
Mosny, M. and Funt, B., "Cubical Gamut Mapping Colour Constancy," Proc. CGIV2010 IS&T Fifth European Conf. on Colour in Graphics,
Imaging and Vision, Joensuu, June 2010.
A new color constancy algorithm called Cubical Gamut
Mapping (CGM) is introduced. CGM is computationally very
simple, yet performs better than many currently known
algorithms in terms of median illumination estimation error.
Moreover, it can be tuned to minimize the maximum error.
Being able to reduce the maximum error, possibly at the
expense of increased median error, is an advantage over many
published color constancy algorithms, which may perform quite
well in terms of median illumination-estimation error, but have
very poor worst-case performance. CGM is based on principles
similar to existing gamut mapping algorithms; however, it
represents the gamut of image chromaticities as a simple cube
characterized by the image’s maximum and minimum rgb
chromaticities rather than their more complicated convex hull.
It also uses the maximal RGBs as an additional source of
information about the illuminant. The estimate of the scene
illuminant is obtained by linearly mapping the chromaticity of
the maximum RGB, minimum rgb and maximum rgb values.
The algorithm is trained off-line on a set of synthetically
generated images. Linear programming techniques for
optimizing the mapping both in terms of the sum of errors and
in terms of the maximum error are used. CGM uses a very
simple image pre-processing stage that does not require image
segmentation. For each pixel in the image, the pixels in the N-
by-N surrounding block are averaged. The pixels for which at
least one of the neighbouring pixels in the N-by-N surrounding
block differs from the average by more than a given threshold
are removed. This pre-processing not only improves CGM, but
also improves the performance of other published algorithms
such as max RGB and Grey World.
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