Gamut Mapping in RGB Colour Spaces with the Iterative Ratios Diffusion Algorithm

Abstract

Different reproduction devices can have different sets of reproducible colours. These sets are called gamuts. The process of transforming colours from one device (or image) gamut to another is called gamut mapping. Gamut mapping has many technical issues to be considered: the used colour space, direction and magnitude of the mapping and whether and to which extent ingamut colours should be altered. Spatially invariant algorithms treat all the pixels independently on their position in the image. Spatially variant (local) algorithms allows a better rendition but introduces the problem of artefacts and/or haloing in the resulting image. In this paper we propose a spatially variant gamut mapping algorithm that creates virtually no artefacts nor haloing in the resulting image. We start from an analysis of the Retinex algorithm and devise proper functionals to build an algorithm which tries to maintain spatial ratios in the image while mapping it into the gamut and, at the same time, avoids all drawbacks of Retinex approaches. We suggest to perform the mapping in an RGB colour space rather than one of the perceptually more homogeneous ones. Although less homogeneous, we experimentally show that RGB colour spaces actually have better hue constancy according to a certain criterion.

Vision Models for Wide Color Gamut Imaging in Cinema

“…In these GRAs, another stage of gamut clipping is integrated to process the resulting image in case the spatial filtering operation places a few pixels outside the destination gamut. Local GRAs that are inspired from the Retinex framework perform spatial comparisons to retain source image gradients in the reproduced images [29], [30], [31], [32]. Some spatial GRAs [33], [34], [35], [36], [37] pose gamut mapping as an optimization scheme where, given a source image and its gamut mapped version, the aim is to keep perturbing the gamut mapped image until its difference w.r.t.…”

Wide Color Gamut Image Content Characterization: Method, Evaluation, and Applications

“…QoE of the gamutreduced image often decreases since the color may become blurred around the pixels where the color changes. The latter considers spatial relationship between pixels at the expense of increased computational complexity in order to enhance perceived quality of the gamut-reduced images [7], [19], [20], [21], [22], [23], [24], [25].…”

Automatic, Fast and Perceptually Accurate Gamut Mapping Based on Vision Science Models

“…GM transforms colors of a source material to the colors of a target device. Generally there exist two possibilities for gamut mapping 1 : if the source gamut is larger than the target gamut, gamut reduction (GR) is needed [2][3][4][5][6][7][8] and if the source gamut is smaller than the target gamut, the process of gamut extension (GE) is desired 9-13 . Gamut mapping is also needed on the image capture side. There exists various consumer and professional movie cameras that are capable of capturing content with different range of colors.…”