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Roy S Berns - One of the best experts on this subject based on the ideXlab platform.
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spectral Color reproduction using an interim connection space based lookup table
Journal of Imaging Science and Technology, 2008Co-Authors: Shohei Tsutsumi, Mitchell R. Rosen, Roy S BernsAbstract:For purposes of defining a feasible approach to spectral Color Management, previous research proposed an interim connection space (ICS). ICS is relatively low in dimension and would be situated between a high-dimensional spectral profile connection space and output units. The current research simulated printed spectra after using a multidimensional ICS-based lookup tables (LUTs) based on LabPQR, an ICS described in earlier work. LabPQR has three Colorimetric dimensions (CIELAB) and additional dimensions to describe a metameric black (PQR). The spectral reproduction accuracies for printing on a six-Color ink jet printer were compared based on several versions of the ICS-based LUTs. Variations were evaluated with respect to quality trade-offs between size of the LUT and spectral reproduction accuracies, as well as the number of dimensions necessary for spectral Color Management. A five-dimensional 17 × 17 × 17 × 5 × 3 LUT performed well with three dimensions for CIELAB and two dimensions for the PQR metameric black space. This LUT resulted in average CIEDE2000 of 0.51 and average spectral root mean square error of 4.22% for a simulated spectral reproduction of the GretagMacbeth ColorChecker.
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spectral gamut mapping framework based on human Color vision
International Conference on Computer Graphics Imaging and Visualisation, 2008Co-Authors: Philipp Urban, Mitchell R. Rosen, Roy S BernsAbstract:A new spectral gamut mapping framework is presented. It adjusts the reproduction, choosing spectra within the printer's gamut that satisfy Colorimetric criteria across a hierarchical set of illuminants. For the most important illuminant a traditional gamut mapping is performed and for each additional considered illuminant Colors are mapped into device and pixel dependent metamer mismatch gamuts. A computational separation method is proposed in order to test the framework. Utilizing this separation method on a seven channel printing system, experiments allowed a deeper view on the structure of the device and pixel dependent metamer mismatch gamuts and the possible directions in Color space in which a potential metameric gamut mapping transformation could map out-of-metameric-gamut Colors. Introduction In recent years spectral acquisition has become an active research eld. Today's technology is able to capture high resolution multichannel images with very small spectral estimation error. This technology is employed by museums for artwork reproduction and for archiving applications. Wide-gamut multiColorant printers are used within traditional Color Management to create accurate Colorimetric reproductions that match originals under a single illuminant. For some applications it can be desireable for reproductions to match originals under multiple illuminants. In such cases a spectral reproduction is needed. A basic limitation of such a reproduction is the physical ability of printing devices to reproduce re ectances. The spectral printer gamut is much smaller than the space of all natural re ectances. A lower bound of the dimensionality of natural re ectances can be determined through analysis of multiple spectral databases [1]. Only by looking onto the dimensionality difference does it become obvious that the majority of spectral re ectances cannot be reproduced without spectral error on a typical printer. It becomes necessary to map the unreproducible spectra into the spectral gamut of the printer. Such a mapping is not unique and an optimal transformation strongly depends on the special application. In recent years various metrics in spectral space have been proposed [2, 3]. To show an advantage compared to traditional Color Management, spectral reproduction should be for one illuminant as visually correct as a Colorimetric reproduction and for other illuminants superior. An approach has been proposed, combining a mapping in a perceptual Color space based on one illuminant and a spectral mapping within the corresponding three dimensional device metameric black space [4, 5, 6, 7]. In this paper we are presenting a spectral gamut mapping framework that adjusts a reproduction so that it matches the original under multiple illuminants considering properties of human Color vision. The Spectral Gamut Mapping Framework Terminology In order to explain the spectral gamut mapping framework we use the common terminology of discrete spectra, resulting from a sampling of the continuous spectra at N equidistant positions within the visible wavelength range from 380 nm to 730 nm. Each re ectance spectrum is a N-dimensional vector r ∈ [0,1]N and the set of illuminants for which the reproduction has to be adjusted is a set of N-dimensional vectors representing the spectral power distributions of the illuminants I1, . . . , In ∈ RN . In the following text we use the observer's CIEXYZ tristimulus X(r, I) as a function of the re ectance r = (r1, . . . ,rN) and the illuminating illuminant I = (I1, . . . , IN): X(r, I) = 1 ∑i=1 ȳiIi ( N ∑ i=1 xiIiri, N ∑ i=1 ȳiIiri, N ∑ i=1 ziIiri )T (1) where x, ȳ, z are the CIE Color matching functions for the 2◦ or 10◦ observer, respectively. The Color space transformation from CIEXYZ into the nearly perceptually uniform CIELAB Color space is denoted by L : CIEXYZ 7→ CIELAB and the inverse function by L−1 : CIELAB 7→ CIEXYZ. The set of all re ectances that result in the CIELAB value x for an illuminant I is called metameric re ectance set and will be denoted by M(x, I) = {r ∈ [0,1]N | L(X(r, I)) = x} (2) The spectral printer gamut, which is the space of all printable spectral re ectances of the given device, is denoted by G ⊂ [0,1]N .
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spectral Color Management using interim connection spaces based on spectral decomposition
Color Imaging Conference, 2006Co-Authors: Shohei Tsutsumi, Mitchell R. Rosen, Roy S BernsAbstract:A feasible approach to spectral Color Management was previously defined to include lookups performed within an interim connection space (ICS). ICS is relatively low in dimensions and is situated between a high-dimensional spectral profile connection space and output units. The definition of ICS axes and the minimum number of ICS dimensions are explored here by considering the LabPQR, an ICS described in earlier research. LabPQR has three Colorimetric dimensions (CIE L*a*b*) and additional dimensions to describe a metameric black (PQR). Several versions of LabPQR are explored. One type defines PQR axes based on metameric blacks generated from Cohen and Kappauf's spectral decomposition. The second type is constructed in an unconstrained way where metameric blacks are statistically derived based on the spectral characteristics of the target output device. For a six-dimensional LabPQR, one that uses three Colorimetric and three metameric black dimensions, it was found that Cohen and Kappauf-based LabPQR was inferior for estimating the spectra when compared with the unconstrained method. However, when the limited spectral gamut of an output device was introduced through printer simulation and necessary spectral gamut mapping, the disadvantage of the six-dimensional Cohen and Kappauf-based LabPQR dissipated. On the other hand, reducing LabPQR to only five-dimensions (two metameric black dimensions) reintroduced the advantage of the unconstrained approach even after simulated printing including spectral gamut mapping. Importantly, it was found that the five-dimensional unconstrained approach achieved equivalent levels of performance to a full 31-dimensional approach within simulated printer spectral gamut limitations. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 282–299, 2008.
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a critical review of spectral models applied to binary Color printing
Color Research and Application, 2000Co-Authors: David R Wyble, Roy S BernsAbstract:A critical review of binary Color printing models is presented. The goal is to provide an understanding of the application of Color printer models as a component for device profiles within a Color Management system. A short description of a modern Color Management system is pre- sented, followed by a brief explanation of the halftoning process. This leads into the discussion of the individual models, which takes an historical approach. The discussion starts with early models proposed in the 1930s by Murray, followed by Neugebauer, Yule and Nielsen, and other much more recent model forms. To aid in gathering the appro- priate data for printer modeling, experimental techniques are then discussed, followed by an explanation of the model optimization methods needed for parameter fitting. The re- view concludes with procedures for model evaluation and a presentation of the results from an application of the models to a sample dataset for an electrophotographic printer.
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building Color Management modules using linear optimization ii prepress system for offset printing
Journal of Imaging Science and Technology, 1998Co-Authors: Koichi Iino, Roy S BernsAbstract:A spectral model was derived to predict the spectral reflectance factor of Colors formed using a Color proofing system simulating offset printing. A first-order model was based on the spectral Neugebauer equation modified by the Yule-Nielsen correction in which n was assumed to vary as a function of wavelength. The n z and effective dot areas were optimized using primary (cyan, magenta, yellow, and black) halftone tints. Systematic errors were observed. The systematic error behaved in a similar fashion to the phenomenon of ink trapping. Because ink trapping,.ink spread, ink mixture and variance of mechanical dot gain were negligible for this proofing system, this is an optical effect to be referred to as optical trapping. An interaction model was derived that compensated for optical trapping. Adding the optical trapping effect to the first-order model significantly improved model prediction to an average ΔE * ab , of 2.2 with a maximum of 5.5. A simple black printer model was derived for an inversion of the forward model that aimed to provide a similar black amount with a conventional Color-separation method and Colorimetric match applying a concept of under-Color removal (UCR) in original density space with tone reproduction curves of a gray scale. Using the Simplex method, the modified spectral Neugebauer model with the black printer model was inverted to build a backward model. Influences of the dot area transform function obtained from the backward model were compared with those from a conventional method for an evaluation of similarity. A desktop drum scanner was Colorimetrically characterized using a spectral reconstruction model for a reflective photographic material to build a concatenated device profile in which digital counts of a scanned photographic reflection print were the input and those of the proofing system were the output. Performances of the concatenated device profile were evaluated for practical use. The average ΔE* ab error from the profile was 2.1 including Colors outside of the proofing system's Color gamut.
Philipp Urban - One of the best experts on this subject based on the ideXlab platform.
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3d printing spatially varying Color and translucency
ACM Transactions on Graphics, 2018Co-Authors: Alan Brunton, Can Ates Arikan, Tejas Madan Tanksale, Philipp UrbanAbstract:We present an efficient and scalable pipeline for fabricating full-Colored objects with spatially-varying translucency from practical and accessible input data via multi-material 3D printing. Observing that the costs associated with BSSRDF measurement and processing are high, the range of 3D printable BSSRDFs are severely limited, and that the human visual system relies only on simple high-level cues to perceive translucency, we propose a method based on reproducing perceptual translucency cues. The input to our pipeline is an RGBA signal defined on the surface of an object, making our approach accessible and practical for designers. We propose a framework for extending standard Color Management and profiling to combined Color and translucency Management using a gamut correspondence strategy we call opaque relative processing. We present an efficient streaming method to compute voxel-level material arrangements, achieving both realistic reproduction of measured translucent materials and artistic effects involving multiple fully or partially transparent geometries.
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spectral gamut mapping framework based on human Color vision
International Conference on Computer Graphics Imaging and Visualisation, 2008Co-Authors: Philipp Urban, Mitchell R. Rosen, Roy S BernsAbstract:A new spectral gamut mapping framework is presented. It adjusts the reproduction, choosing spectra within the printer's gamut that satisfy Colorimetric criteria across a hierarchical set of illuminants. For the most important illuminant a traditional gamut mapping is performed and for each additional considered illuminant Colors are mapped into device and pixel dependent metamer mismatch gamuts. A computational separation method is proposed in order to test the framework. Utilizing this separation method on a seven channel printing system, experiments allowed a deeper view on the structure of the device and pixel dependent metamer mismatch gamuts and the possible directions in Color space in which a potential metameric gamut mapping transformation could map out-of-metameric-gamut Colors. Introduction In recent years spectral acquisition has become an active research eld. Today's technology is able to capture high resolution multichannel images with very small spectral estimation error. This technology is employed by museums for artwork reproduction and for archiving applications. Wide-gamut multiColorant printers are used within traditional Color Management to create accurate Colorimetric reproductions that match originals under a single illuminant. For some applications it can be desireable for reproductions to match originals under multiple illuminants. In such cases a spectral reproduction is needed. A basic limitation of such a reproduction is the physical ability of printing devices to reproduce re ectances. The spectral printer gamut is much smaller than the space of all natural re ectances. A lower bound of the dimensionality of natural re ectances can be determined through analysis of multiple spectral databases [1]. Only by looking onto the dimensionality difference does it become obvious that the majority of spectral re ectances cannot be reproduced without spectral error on a typical printer. It becomes necessary to map the unreproducible spectra into the spectral gamut of the printer. Such a mapping is not unique and an optimal transformation strongly depends on the special application. In recent years various metrics in spectral space have been proposed [2, 3]. To show an advantage compared to traditional Color Management, spectral reproduction should be for one illuminant as visually correct as a Colorimetric reproduction and for other illuminants superior. An approach has been proposed, combining a mapping in a perceptual Color space based on one illuminant and a spectral mapping within the corresponding three dimensional device metameric black space [4, 5, 6, 7]. In this paper we are presenting a spectral gamut mapping framework that adjusts a reproduction so that it matches the original under multiple illuminants considering properties of human Color vision. The Spectral Gamut Mapping Framework Terminology In order to explain the spectral gamut mapping framework we use the common terminology of discrete spectra, resulting from a sampling of the continuous spectra at N equidistant positions within the visible wavelength range from 380 nm to 730 nm. Each re ectance spectrum is a N-dimensional vector r ∈ [0,1]N and the set of illuminants for which the reproduction has to be adjusted is a set of N-dimensional vectors representing the spectral power distributions of the illuminants I1, . . . , In ∈ RN . In the following text we use the observer's CIEXYZ tristimulus X(r, I) as a function of the re ectance r = (r1, . . . ,rN) and the illuminating illuminant I = (I1, . . . , IN): X(r, I) = 1 ∑i=1 ȳiIi ( N ∑ i=1 xiIiri, N ∑ i=1 ȳiIiri, N ∑ i=1 ziIiri )T (1) where x, ȳ, z are the CIE Color matching functions for the 2◦ or 10◦ observer, respectively. The Color space transformation from CIEXYZ into the nearly perceptually uniform CIELAB Color space is denoted by L : CIEXYZ 7→ CIELAB and the inverse function by L−1 : CIELAB 7→ CIEXYZ. The set of all re ectances that result in the CIELAB value x for an illuminant I is called metameric re ectance set and will be denoted by M(x, I) = {r ∈ [0,1]N | L(X(r, I)) = x} (2) The spectral printer gamut, which is the space of all printable spectral re ectances of the given device, is denoted by G ⊂ [0,1]N .
Mitchell R. Rosen - One of the best experts on this subject based on the ideXlab platform.
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spectral gamut mapping framework based on human Color vision
International Conference on Computer Graphics Imaging and Visualisation, 2008Co-Authors: Philipp Urban, Mitchell R. Rosen, Roy S BernsAbstract:A new spectral gamut mapping framework is presented. It adjusts the reproduction, choosing spectra within the printer's gamut that satisfy Colorimetric criteria across a hierarchical set of illuminants. For the most important illuminant a traditional gamut mapping is performed and for each additional considered illuminant Colors are mapped into device and pixel dependent metamer mismatch gamuts. A computational separation method is proposed in order to test the framework. Utilizing this separation method on a seven channel printing system, experiments allowed a deeper view on the structure of the device and pixel dependent metamer mismatch gamuts and the possible directions in Color space in which a potential metameric gamut mapping transformation could map out-of-metameric-gamut Colors. Introduction In recent years spectral acquisition has become an active research eld. Today's technology is able to capture high resolution multichannel images with very small spectral estimation error. This technology is employed by museums for artwork reproduction and for archiving applications. Wide-gamut multiColorant printers are used within traditional Color Management to create accurate Colorimetric reproductions that match originals under a single illuminant. For some applications it can be desireable for reproductions to match originals under multiple illuminants. In such cases a spectral reproduction is needed. A basic limitation of such a reproduction is the physical ability of printing devices to reproduce re ectances. The spectral printer gamut is much smaller than the space of all natural re ectances. A lower bound of the dimensionality of natural re ectances can be determined through analysis of multiple spectral databases [1]. Only by looking onto the dimensionality difference does it become obvious that the majority of spectral re ectances cannot be reproduced without spectral error on a typical printer. It becomes necessary to map the unreproducible spectra into the spectral gamut of the printer. Such a mapping is not unique and an optimal transformation strongly depends on the special application. In recent years various metrics in spectral space have been proposed [2, 3]. To show an advantage compared to traditional Color Management, spectral reproduction should be for one illuminant as visually correct as a Colorimetric reproduction and for other illuminants superior. An approach has been proposed, combining a mapping in a perceptual Color space based on one illuminant and a spectral mapping within the corresponding three dimensional device metameric black space [4, 5, 6, 7]. In this paper we are presenting a spectral gamut mapping framework that adjusts a reproduction so that it matches the original under multiple illuminants considering properties of human Color vision. The Spectral Gamut Mapping Framework Terminology In order to explain the spectral gamut mapping framework we use the common terminology of discrete spectra, resulting from a sampling of the continuous spectra at N equidistant positions within the visible wavelength range from 380 nm to 730 nm. Each re ectance spectrum is a N-dimensional vector r ∈ [0,1]N and the set of illuminants for which the reproduction has to be adjusted is a set of N-dimensional vectors representing the spectral power distributions of the illuminants I1, . . . , In ∈ RN . In the following text we use the observer's CIEXYZ tristimulus X(r, I) as a function of the re ectance r = (r1, . . . ,rN) and the illuminating illuminant I = (I1, . . . , IN): X(r, I) = 1 ∑i=1 ȳiIi ( N ∑ i=1 xiIiri, N ∑ i=1 ȳiIiri, N ∑ i=1 ziIiri )T (1) where x, ȳ, z are the CIE Color matching functions for the 2◦ or 10◦ observer, respectively. The Color space transformation from CIEXYZ into the nearly perceptually uniform CIELAB Color space is denoted by L : CIEXYZ 7→ CIELAB and the inverse function by L−1 : CIELAB 7→ CIEXYZ. The set of all re ectances that result in the CIELAB value x for an illuminant I is called metameric re ectance set and will be denoted by M(x, I) = {r ∈ [0,1]N | L(X(r, I)) = x} (2) The spectral printer gamut, which is the space of all printable spectral re ectances of the given device, is denoted by G ⊂ [0,1]N .
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spectral Color reproduction using an interim connection space based lookup table
Journal of Imaging Science and Technology, 2008Co-Authors: Shohei Tsutsumi, Mitchell R. Rosen, Roy S BernsAbstract:For purposes of defining a feasible approach to spectral Color Management, previous research proposed an interim connection space (ICS). ICS is relatively low in dimension and would be situated between a high-dimensional spectral profile connection space and output units. The current research simulated printed spectra after using a multidimensional ICS-based lookup tables (LUTs) based on LabPQR, an ICS described in earlier work. LabPQR has three Colorimetric dimensions (CIELAB) and additional dimensions to describe a metameric black (PQR). The spectral reproduction accuracies for printing on a six-Color ink jet printer were compared based on several versions of the ICS-based LUTs. Variations were evaluated with respect to quality trade-offs between size of the LUT and spectral reproduction accuracies, as well as the number of dimensions necessary for spectral Color Management. A five-dimensional 17 × 17 × 17 × 5 × 3 LUT performed well with three dimensions for CIELAB and two dimensions for the PQR metameric black space. This LUT resulted in average CIEDE2000 of 0.51 and average spectral root mean square error of 4.22% for a simulated spectral reproduction of the GretagMacbeth ColorChecker.
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Color Management of Four-Primary Digital Light Processing Projectors
Journal of Imaging Science and Technology, 2006Co-Authors: David R Wyble, Mitchell R. RosenAbstract:Traditionally, hardware for additive Color displays, including projection devices, has been built from a set of only three primaries: a red, a green, and a blue. Recently, some manufacturers of projector displays have designed their hardware to project a fourth primary, a white. This fourth primary has been helpful in increasing the luminous output possible from these displays. Because interdevice Color communication infrastructure is based on red, green, and blue channels (RGB), the four-primary devices accept RGB digits and internally convert to red, green, blue, and white channels (RGBW). From a Color Management viewpoint, the four-Color projectors look like RGB devices, but the typical Color characterization models fail owing to the complexity introduced by the hidden RGB to RGBW conversion. Several four-primary digital light processing projectors were investigated and a new characterization model is proposed that approximately accounts for the relationship between RGB digital counts and resultant projected Colorimetry.
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spectral Colorimetry using labpqr an interim connection space
Journal of Imaging Science and Technology, 2006Co-Authors: Maxim W Derhak, Mitchell R. RosenAbstract:A method is presented for deriving a conversion from reflectance spectra to a convenient intermediate form introduced as LabPQR. LabPQR is designed for use within a spectral Color Management system as an interim connection space (ICS) between a reflectance based profile connection space (PCS) and output device digit counts. The LabPQR ICS makes use of a spectral encoding that explicitly incorporates Colorimetry. The initial three dimensions of a LabPQR ICS provide a Colorimetric representation of reflectance spectra under an illuminant. Additional dimensions define spectral corrections that allow inverse transformation to approximately the original spectra. Consistent with a previously defined spectral Color Management transformation chain, the PQR dimensions of the ICS can be optimally formulated to suit any specific output device's spectral rendering capabilities. After describing a method for distilling spectra into, and reconstituting spectra, from LabPQR coordinates, several sample transformations are demonstrated using various output devices. Visualizations follow of some sample LabPQR gamuts. Observations are made, and from these observations, a possible method for performing spectral gamut mapping is proposed. Current methods of Color Management (specifically using ICC profiles) are discussed relative to LabPQR visualization. In conclusion, proposals are made for future research and possibilities.
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spectral Color Management using interim connection spaces based on spectral decomposition
Color Imaging Conference, 2006Co-Authors: Shohei Tsutsumi, Mitchell R. Rosen, Roy S BernsAbstract:A feasible approach to spectral Color Management was previously defined to include lookups performed within an interim connection space (ICS). ICS is relatively low in dimensions and is situated between a high-dimensional spectral profile connection space and output units. The definition of ICS axes and the minimum number of ICS dimensions are explored here by considering the LabPQR, an ICS described in earlier research. LabPQR has three Colorimetric dimensions (CIE L*a*b*) and additional dimensions to describe a metameric black (PQR). Several versions of LabPQR are explored. One type defines PQR axes based on metameric blacks generated from Cohen and Kappauf's spectral decomposition. The second type is constructed in an unconstrained way where metameric blacks are statistically derived based on the spectral characteristics of the target output device. For a six-dimensional LabPQR, one that uses three Colorimetric and three metameric black dimensions, it was found that Cohen and Kappauf-based LabPQR was inferior for estimating the spectra when compared with the unconstrained method. However, when the limited spectral gamut of an output device was introduced through printer simulation and necessary spectral gamut mapping, the disadvantage of the six-dimensional Cohen and Kappauf-based LabPQR dissipated. On the other hand, reducing LabPQR to only five-dimensions (two metameric black dimensions) reintroduced the advantage of the unconstrained approach even after simulated printing including spectral gamut mapping. Importantly, it was found that the five-dimensional unconstrained approach achieved equivalent levels of performance to a full 31-dimensional approach within simulated printer spectral gamut limitations. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 282–299, 2008.
Fred Bunting - One of the best experts on this subject based on the ideXlab platform.
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real world Color Management industrial strength production techniques
2005Co-Authors: Bruce Fraser, Chris Murphy, Fred BuntingAbstract:Every graphics professional worth his or her salt knows the importance of Color Management. No matter how much thought artist and client put into the Color scheme for a given project, all of that work is for naught if you can't get your results to match your expectations. Enter Real World Color Management, Second Edition. In this thoroughly updated under-the-hood reference, authors Bruce Fraser, Chris Murphy, and Fred Bunting draw on their years of professional experience to show you everything you need to know about Color Management. Whether your final destination is print, Web, or film, Real World Color Management, Second Edition takes the mystery out of Color Management, covering everything from Color theory and Color models to understanding how devices interpret and display Color. You'll find expert advice for building and fine-tuning Color profiles for input and output devices (digital cameras and scanners, displays, printers, and more), selecting the right Color Management workflow, and managing Color within and across major design applications. Get Real World Color Management, Second Edition--and get ready to dazzle!
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real world Color Management
2003Co-Authors: Bruce Fraser, Chris Murphy, Fred BuntingAbstract:From the Publisher: Graphic arts professionals know that the goal of maintaining Color fidelity throughout a print or Web design project can be an elusive one. What you see on the screen might look like the sample you hold in your hand, but what about the final product? From swatch to screen, from proof to final output, the way to make sure your results match your expectations -- and those of your clients -- is through Color Management, and Real World Color Management is the industrial-strength, under-the-hood guide. Industry experts Bruce Fraser, Chris Murphy, and Fred Bunting pack every page with tips and techniques to help you build and fine-tune ICC profiles, develop a Color-managed workflow, understand the craft of converting from one Color space to another, and manage Color using popular graphics applications. You'll also gain insight into Color theory and Color models, and learn how various devices interpret, display, and reproduce Color.
Erik Reinhard - One of the best experts on this subject based on the ideXlab platform.
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a gamut mapping framework for Color accurate reproduction of hdr images
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Elena Sikudova, Tania Pouli, Alessandro Artusi, Ahmet Oguz Akyuz, Francesco Banterle, Zeynep Miray Mazlumoglu, Erik ReinhardAbstract:Few tone mapping operators (TMOs) take Color Management into consideration, limiting compression to luminance values only. This may lead to changes in image chroma and hues which are typically managed with a post-processing step. However, current post-processing techniques for tone reproduction do not explicitly consider the target display gamut. Gamut mapping on the other hand, deals with mapping images from one Color gamut to another, usually smaller, gamut but has traditionally focused on smaller scale, chromatic changes. In this context, we present a novel gamut and tone Management framework for Color-accurate reproduction of high dynamic range (HDR) images, which is conceptually and computationally simple, parameter-free, and compatible with existing TMOs. In the CIE LCh Color space, we compress chroma to fit the gamut of the output Color space. This prevents hue and luminance shifts while taking gamut boundaries into consideration. We also propose a compatible lightness compression scheme that minimizes the number of Color space conversions. Our results show that our gamut Management method effectively compresses the chroma of tone mapped images, respecting the target gamut and without reducing image quality.
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a gamut mapping framework for Color accurate reproduction of hdr images
IEEE Computer Graphics and Applications, 2016Co-Authors: Elena Sikudova, Tania Pouli, Alessandro Artusi, Ahmet Oguz Akyuz, Francesco Banterle, Zeynep Miray Mazlumoglu, Erik ReinhardAbstract:Few tone mapping operators (TMOs) take Color Management into consideration, limiting compression to luminance values only. This could lead to changes in image chroma and hues, which are typically managed with a post-processing step. However, current post-processing techniques for tone reproduction do not explicitly consider the target display gamut. Gamut mapping, on the other hand, deals with mapping images from one Color gamut to another, usually smaller, gamut but has traditionally focused on smaller scale, chromatic changes. The authors present a combined gamut- and tone-Management framework for Color-accurate reproduction of high dynamic range images that can prevent hue and luminance shifts while taking gamut boundaries into consideration. Their approach is conceptually and computationally simple, parameter-free, and compatible with existing TMOs.