Estimation of nonlinear transfer curves for conversion of color images to a known color space

This paper presents a supervised algorithm for estimating the unknown nonlinearity undergone by the three color components of an image in the image acquisition process. The algorithm is based on the rank-one hypothesis, which postulates that the linear tristimulus values in a region of uniform surface color lie on a straight line through the origin. An objective function is formulated whose minimization yields the estimate of the unknown nonlinearity. Images corrected with the estimated inverse nonlinearity are shown to exhibit chromatic properties that are much more piecewise constant that in the original image. This property will be very useful in quantization and segmentation applications