We treat image-to-device gamut mapping as a multi-criteria optimization problem. Our approach leads to a parameterized mathematical optimization problem that allows to constrain the degree to which objectives like contrast preservation, hue preservation, saturation preservation and the continuity of the mapping can be violated while maximizing the device gamut exploitation. We demonstrate the feasibility of our approach on several benchmark image- and device gamuts.
We present a new method for the computation of both, image and device gamut boundaries. The method has been designed to bypass the quality vs. time trade off that one usually faces when computing gamut boundaries. This trade off is between the geometric accuracy of the boundary and the time it takes to compute it. Our method is geometrically accurate in the sense that the computed gamut boundary tightly encloses the color points that make up the gamut. At the same time it is fast compared to other methods. Thus it can be used in an image-dependent gamut mapping approach. The underlying concept of the presented method is a data structure that we call discrete flow complex which is derived from the discrete distance function to the color points. We have implemented the method and tested it with a suite of test images. Our experimental results show that the method is in fact fast and geometrically accurate. In the future we plan to use the gamut boundaries computed by our method for fast, high-quality, image-dependent gamut mapping in three dimensions.