A Gamut-Mapping Framework for Color-Accurate Reproduction of HDR Images

Gamut mapping is the problem of transforming the colors of image or video content so as to fully exploit the color palette of the display device where the content will be shown, while preserving the artistic intent of the original content's creator. In particular in the cinema industry, the rapid advancement in display technologies has created a pressing need to develop automatic and fast gamut mapping algorithms. In this paper we propose a novel framework that is based on vision science models, performs both gamut reduction and gamut extension, is of low computational complexity, produces results that are free from artifacts and outperforms state-of-the-art methods according to psychophysical tests. Our experiments also highlight the limitations of existing objective metrics for the gamut mapping problem.Index Terms-Gamut mapping algorithms, wide gamut imaging, color reproduction, vision models for color and contrast, gamut mapping for cinema. !This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

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“…and then processed with our GM algorithm. This is consistent with the workflow for GM of HDR content proposed in [73].…”

Section: The Hdr Casesupporting

confidence: 90%

Vision Models for Wide Color Gamut Imaging in Cinema

Zamir

Vázquez-Corral

Bertalmío

2021

IEEE Trans. Pattern Anal. Mach. Intell.

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“…These techniques have been mainly developed to overcome the limited mismatch between color gamuts, i.e., LDR image and display. Only recently techniques dealing with HDR content are starting to appear [17]. However, the aim of our gamut correction is two folds.…”

Section: Gamut Correctionmentioning

confidence: 99%

Automatic saturation correction for dynamic range management algorithms

Artusi

Pouli

Banterle

et al. 2018

Signal Processing: Image Communication

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High dynamic range (HDR) images require tone reproduction to match the range of values to the capabilities of a display. For computational reasons and given the absence of fully calibrated imagery, rudimentary color reproduction is often added as a postprocessing step rather than integrated into tone reproduction algorithms. In the general case, this currently requires manual parameter tuning, and can be automated only for some global tone reproduction operators by inferring parameters from the tone curve. We present a novel and fully automatic saturation correction technique, suitable for any tone reproduction operator (including inverse tone reproduction), which exhibits fewer distortions in hue and luminance reproduction than the current state-of-the-art. We validated its comparative effectiveness through subjective experiments and objective metrics. Our experiments confirm that saturation correction significantly contributes toward the perceptually plausible color reproduction of tonemapped content and would, therefore, be useful in any color-critical application.

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“…This clipping may again introduce hue shifts and image defects. [9]. To improve the saturation of the tone mapped image, a simple solution is to introduce an adjustable parameter which allows to control the overall saturation of the tone mapped image [10].…”

Section: A Problem Statmentmentioning

confidence: 99%

“…In particular, HDR imagery is conveying to the end-user an extraordinary experience, when compared to the traditional digital imaging as known today, e.g., [8][9][10] …”

Section: What We Have Learntmentioning

confidence: 99%