  In the image processing pipeline of almost every digital camera there is a
part dedicated to computational color constancy i.e. to removing the influence
of illumination on the colors of the image scene. Some of the best known
illumination estimation methods are the so called statistics-based methods.
They are less accurate than the learning-based illumination estimation methods,
but they are faster and simpler to implement in embedded systems, which is one
of the reasons for their widespread usage. Although in the relevant literature
it often appears as if they require no training, this is not true because they
have parameter values that need to be fine-tuned in order to be more accurate.
In this paper it is first shown that the accuracy of statistics-based methods
reported in most papers was not obtained by means of the necessary
cross-validation, but by using the whole benchmark datasets for both training
and testing. After that the corrected results are given for the best known
benchmark datasets. Finally, the so called green stability assumption is
proposed that can be used to fine-tune the values of the parameters of the
statistics-based methods by using only non-calibrated images without known
ground-truth illumination. The obtained accuracy is practically the same as
when using calibrated training images, but the whole process is much faster.
The experimental results are presented and discussed. The source code is
available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.
