Gamut Mapping in Cinematography Through Perceptually-Based Contrast Modification

Abstract

Gamut mapping transforms the colors of an input image to the colors of a target device so as to exploit the full potential of the rendering device in terms of color rendition. In this paper we present spatial gamut mapping algorithms that rely on a perceptually-based variational framework. Our algorithms adapt a well-known image energy functional whose minimization leads to image enhancement and contrast modification. We show how by varying the importance of the contrast term in the image functional we are able to perform gamut reduction and gamut extension. We propose an iterative scheme that allows our algorithms to successfully map the colors from the gamut of the original image to a given destination gamut while keeping the perceived colors close to the original image. Both subjective and objective evaluations validate the promising results achieved via our proposed algorithms.

Duality Principle for Image Regularization and Perceptual Color Correction Models

“…We also observe that the negative component in the regularization model behaves like the positive component in the contrast enhancement model and vice versa, which is coherent with the fact that the models only differ by the sign of the anisotropic nonlocal total variation. Note that by our choice of the parameter w, the regularization model is acting as a contrast reduction model, which is actually the vectorial extension of the model used by Zamir et al [25] for color gamut reduction purpose.…”

“…Regarding contrast modification, the pioneer variational approach is due to Sapiro and Caselles [23], who performed contrast enhancement for histogram equalization purpose. Since then, this variational formulation has been generalized and applied in different contexts: perceptual color correction [4], [20], tone mapping [14], and gamut mapping [25] to name a few. Connections have also been made between the variational formulation [4] and Retinex theory [5], [6].…”

Vision Models for Wide Color Gamut Imaging in Cinema

“…the source image is minimized according to an error metric. Finally, an image energy functional [38] is introduced to decrease the contrast of the input image in order to perform gamut reduction. Global GEAs: While the majority of the published GMAs deal with the problem of gamut reduction, the case is very different for gamut extension: only a few works have been proposed in this direction.…”

“…The multilevel GEA [54] in its first stage extends the source gamut using a non-linear huevarying function, and in the second stage applies an imagedependent chroma smoothing operation to avoid an overenhancement of contrast and to preserve detail in the final image. Recent works [38], [55], [56] perform spatial gamut extension using partial differential equations. In particular, the contrast of the input image is enhanced by minimizing an energy functional [38]; a monotonically increasing function [55] is applied on the saturation channel of the input image in HSV color space that allows to increase contrast without decreasing the image saturation values; and the GEA [56] operates only on the chromatic components of CIELAB color space, while taking into account the analysis of distortions in hue, chroma and saturation.…”

Enhanced Variational Image Dehazing

“…(3.1) is not adequate, since we want to respect the colors of the haze-free image, not to correct the illuminant of the scene. Different modifications of this hypothesis have already been proposed for several problems [10,50]. Here, to approximately predict which should be the mean value of a dehazed scene, we rely on the model of Eq.…”