Spatial gamut mapping among non-inclusive gamuts

Abstract

Gamut mapping transforms the color gamut of an image to that of a target device. Two cases are usually considered: gamut reduction (target gamut smaller than source gamut), and gamut extension (target gamut larger than the source gamut). Less attention is devoted to the more general case, when neither gamut is fully included in the other. In this work we unify and expand two recent methods for gamut extension and reduction, so as to simultaneously perform both forms of gamut mapping in different regions of the same image without introducing color artifacts or halos. We demonstrate the usefulness of this approach for the traditional gamut mapping problem, and also how the proposed method can be used to adapt the color palette of an image so that it is closer to that of a given reference image. Results are compared with the state-of-the-art and validated through user tests and objective metrics.

Perceived Color Gamut in Images: From Boundary to Difference

“…A similar framework has also been recently proposed (Lee et al, 2020 ). While the simplistic and colorimetric comparison between two images has been shown to be predictive, future work can explore image color appearance modeling (Johnson et al, 2010 ) and spatial gamut mapping (Bonnier et al, 2006 ; Vazquez-Corral and Bertalmío, 2018 ), especially when there are more variations in display parameters and image contents.…”

Coupled Retinex

“…Moreover, the Retinex processed image also looks enhanced in terms of contrast. Retinex has also been used in algorithms for shadow removal, dehazing and gamut mapping [3,4,5,6,7,8]. For the rest paper we will extend the basic Retinex approach for image enhancement (though we note that the modifications we propose might also prove useful for the other applications listed).…”