The objective of colour mapping or colour transfer methods is to recolour a given image or video by deriving a mapping between that image and another image serving as a reference. These methods have received considerable attention in recent years, both in academic literature and industrial applications. Methods for recolouring images have often appeared under the labels of colour correction, colour transfer or colour balancing, to name a few, but their goal is always the same: mapping the colours of one image to another. In this paper, we present a comprehensive overview of these methods and offer a classification of current solutions depending not only on their algorithmic formulation but also their range of applications. We also provide a new dataset and a novel evaluation technique called 'evaluation by colour mapping roundtrip'. We discuss the relative merit of each class of techniques through examples and show how colour mapping solutions can have been applied to a diverse range of problems.

We propose a color mapping method that compensates color differences between images having a common semantic content such as multiple views of a scene taken from different viewpoints. A so-called color mapping model is usually estimated from color correspondences selected from those images. In this work, we introduce a color mapping that model color change in two steps: first, nonlinear, channel-wise mapping; second, linear, cross-channel mapping. Additionally, unlike many state of the art methods, we estimate the model from sparse matches and do not require dense geometric correspondences. We show that well known cross-channel color change can be estimated from sparse color correspondence. Quantitative and visual benchmark tests show good performance compared to recent methods in literature.

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In the context of stereo video, disparity-coherent watermarking has been introduced to provide superior robustness against virtual view synthesis, as well as to improve perceived fidelity. Still, a number of practical considerations have been overlooked and in particular the role of the underlying depth estimation tool on performances. In this article, we explore the interplay between various stereo video processing primitives and highlight a few take away lessons that should be accounted for to improve performances of future disparity-coherent watermarking systems. In particular, we highlight how lost correspondences during the stereo warping process impact watermark detection, thereby calling for innovative designs.

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