  In this paper, we propose a color to grayscale image conversion algorithm
(C2G) that aims to preserve the perceptual properties of the color image as
much as possible. To this end, we propose measures for two perceptual
properties based on contemporary research in vision science: brightness and
multi-scale contrast. The brightness measurement is based on the idea that the
brightness of a grayscale image will affect the perception of the probability
of color information. The color contrast measurement is based on the idea that
the contrast of a given pixel to its surroundings can be measured as a linear
combination of color contrast at different scales. Based on these measures we
propose a graph based optimization framework to balance the brightness and
contrast measurements. To solve the optimization, an $\ell_1$-norm based method
is provided which converts color discontinuities to brightness discontinuities.
To validate our methods, we evaluate against the existing \cadik and Color250
datasets, and against NeoColor, a new dataset that improves over existing C2G
datasets. NeoColor contains around 300 images from typical C2G scenarios,
including: commercial photograph, printing, books, magazines, masterpiece
artworks and computer designed graphics. We show improvements in metrics of
performance, and further through a user study, we validate the performance of
both the algorithm and the metric.
