Colour Gamut Mapping as a Constrained Variational Problem

We present a novel, computationally efficient, iterative, spatial gamut mapping algorithm. The proposed algorithm offers a compromise between the colorimetrically optimal gamut clipping and the most successful spatial methods. This is achieved by the iterative nature of the method. At iteration level zero, the result is identical to gamut clipping. The more we iterate the more we approach an optimal, spatial, gamut mapping result. Optimal is defined as a gamut mapping algorithm that preserves the hue of the image colours as well as the spatial ratios at all scales. Our results show that as few as five iterations are sufficient to produce an output that is as good or better than that achieved in previous, computationally more expensive, methods. Being able to improve upon previous results using such low number of iterations allows us to state that the proposed algorithm is O(N), N being the number of pixels. Results based on a challenging small destination gamut supports our claims that it is indeed efficient.