Spectral Gamut Mapping Research Articles

Charamer mismatch-based spectral gamut mapping

This paper proposes a new spectral gamut mapping framework based on a subspace clustering strategy. Using a node address array-based lookup table, the scheme applies an initial partitioning of the spectral color space of a device into several subspaces with high degrees of correlation between spectral characteristics to enable the construction of point clusters. The Wyszecki hypothesis is then applied to break the spectrum of each point cluster into a fundamental spectrum and a metameric black spectrum, which are both converted to a LabPQR space in which the conventional 6D spatial mapping problem is simplified to 3D mapping within the PQR gamut. Experimental results indicate that the proposed framework improves the effectiveness of spectral gamut mapping by reducing the spectral artificial error while enhancing spatial visualization.

Spatio-Spectral Gamut Mapping and Separation

Spectral printing aims to achieve an illuminant-invariant match between the original and the reproduction. Due to limited printer spectral gamuts, an errorless spectral reproduction is mostly impossible, and spectral gamut mapping is required to reduce perceptual errors. The recently proposed paramermismatch-based spectral gamut mapping (PMSGM) strategy minimizes such errors. However, due to its pixel-wise processing, it may result in severely different tonal values for spectrally similar adjacent pixels, causing unwanted edges (banding) in the final printout. While the addition of some noise to the a* and b* channels of the colorimetric (e.g., CIELAB) image—rendered for the first illuminant—prior to gamut mapping solves the banding problem, it adversely increases the image graininess. In this article, the authors combine the PMSGM strategy with subsequent spectral separation, considering the spatial neighborhood within the tonal-value space and the illuminant-dependent perceptual spaces to directly compute tonal values. Their results show significant improvements to the PMSGM method in terms of avoiding banding artifacts.

Spatio-Spectral Gamut Mapping and Separation

Spectral printing aims to achieve an illuminant-invariant match between the original and the reproduction. Due to limited printer spectral gamuts, an errorless spectral reproduction is mostly impossible, and spectral gamut mapping is required to reduce perceptual errors. The recently proposed paramer-mismatch-based spectral gamut mapping (PMSGM) strategy minimizes such errors. However, due to its pixel-wise processing, it may result in severely different tonal values for spectrally similar adjacent pixels, causing unwanted edges (banding) in the final printout. While the addition of some noise to the a* and b* channels of the colorimetric (e.g., CIELAB) image—rendered for the first illuminant—prior to gamut mapping solves the banding problem, it adversely increases the image graininess. In this article, the authors combine the PMSGM strategy with subsequent spectral separation, considering the spatial neighborhood within the tonal-value space and the illuminant-dependent perceptual spaces to directly compute tonal values. Their results show significant improvements to the PMSGM method in terms of avoiding banding artifacts.

Colour Printing 7.0: Next Generation Multi-Channel Printing

Colour Printing 7.0: Next Generation Multi-Channel Printing (CP7.0) is a training and research project funded under the Marie Skłodowska-Curie Initial Training Networks (MCITN) call in EU's seventh framework programme (FP7). The project is led by Gjøvik University College in collaboration with five full network partners and six associated partners from academia and industry. The project addresses a significant need for research, training and innovation in the printing industry. The main objectives of this project are to train a new generation of printing scientists who will be able to assume science and technology leadership in this established technological sector, and to do research in the colour printing field by fully exploring the possibilities of using more than the conventional CMYK inks. The research focuses particularly on spectral reproduction (new spectral colour modelling, spectral gamut mapping, halftoning and image quality assessment) and on multilayering printing methods to control ink mixing, relief (2.5 D prints) and surface properties. This paper reviews the achievements of the project so far in conjunction with a topical workshop at the 22nd Colour and Imaging Conference on “Next generation colour printing”.

Image-Difference Prediction: From Color to Spectral

We propose a new strategy to evaluate the quality of multi and hyperspectral images, from the perspective of human perception. We define the spectral image difference as the overall perceived difference between two spectral images under a set of specified viewing conditions (illuminants). First, we analyze the stability of seven image-difference features across illuminants, by means of an information-theoretic strategy. We demonstrate, in particular, that in the case of common spectral distortions (spectral gamut mapping, spectral compression, spectral reconstruction), chromatic features vary much more than achromatic ones despite considering chromatic adaptation. Then, we propose two computationally efficient spectral image difference metrics and compare them to the results of a subjective visual experiment. A significant improvement is shown over existing metrics such as the widely used root-mean square error.

Spatially Resolved Joint Spectral Gamut Mapping and Separation

Spatially Resolved Joint Spectral Gamut Mapping and Separation

Framework Design of Multispectral Color Management

It briefly analyzed the disadvantages of ICC (International Color Consortium) color management technology and color management technology based on iCAM (image Color Appearance Model), and then it put forward the thought of multi-spectral color management technology. It specifically designed the basic framework of the multispectral color management, focused on the basic composition and working principle of spectral color management model. Besides it analyzed the composition and basic principle of spectral color gamut mapping framework. Lastly it pointed out the research direction of multispectral color management.

Paramer Mismatch-Based Spectral Gamut Mapping

A spectral agreement between the original scene and a printed reproduction is required to achieve an illuminant-invariant visual match. This is usually impossible since the spectral gamut of typical printing systems is only a small subset of all natural reflectances. Out-of gamut reflectances need to be mapped into the spectral gamut of the printer minimizing the perceived error between original and reproduction for more than one illuminant. In this paper, we propose an algorithmic framework for spectral gamut mapping to achieve a reproduction that is as visually correct as a colorimetric reproduction for one illuminant and is superior for a set of other illuminants. A sequence of hierarchical mappings in 3-D color spaces are performed utilizing the observer's color quantization to increase the spectral variability of subsequent transformations: For the most important illuminant a traditional colorimetric gamut mapping is performed. For any additional illuminants colors are mapped onto pixel-dependent paramer mismatch gamuts preserving the visual equivalence of previous transformations. We present a separation method for investigating the spectral gamut mapping framework and show that hue shifts and chroma gains cannot be always avoided for the second and subsequent illuminants and that the order of illuminants has a large impact on the final reproduction.

Comparing LabPQR and the Spectral Gamut Mapping Framework

Comparing LabPQR and the Spectral Gamut Mapping Framework

Spectral color reproduction minimizing spectral and perceptual color differences

AbstractIn this article, we are combining minimization criteria in the colorant separation process for spectral color reproduction. The colorant separation is performed by inverting a spectral printer model: the spectral Yule‐Nielsen modified Neugebauer model. The inversion of the spectral printer model is an optimization operation in which a criterion is minimized at each iteration. The approach we proposed minimizes a criterion defined by the weighted sum of a spectral difference and a perceptual color difference. The weights can be tuned with a parameter α ∞ [0, 1]. Our goal is to decrease the spectral difference between the original data and its reproduction and also to consider perceptual color difference under different illuminant conditions. In order to find the best α value, we initially compare a pure colorimetric criterion and a pure spectral criterion for the reproduction, then we combine them. We perform four colorant separations: the first separation will minimize the 1976 CIELAB color difference where four illuminants are tested, the second separation will minimize an equally weighted summation of 1976 CIELAB color difference with the four illuminants tested independently, the third colorant separation will minimize a spectral difference, and the fourth colorant separation will combine a weighted sum of a spectral difference and one of the two first colorimetric differences previously introduced. This last colorant separation can be tuned with a parameter in order to emphasize on spectral or colorimetric difference. We use a six colorants printer with artificial inks for our experiments. The prints are simulated by the spectral Yule‐Nielsen modified Neugebauer model. Two groups of data are used for our experiments. The first group describes the data printed by our printing system, which is represented by a regular grid in colorant space of the printer and the second group describes the data which is not originally produced by our printing system but mapped to the spectral printer gamut. The Esser test chart and the Macbeth Color Checker test chart have been selected for the second group. Spectral gamut mapping of this data is carried out before performing colorant separation. Our results show improvement for the colorant separations combining a sum of 1976 CIELAB color difference for a set of illuminants and for the colorant separation combining a sum of 1976 CIELAB color difference and spectral difference, especially in the case of spectral data originally produced by the printer. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 494–504, 2008

Spectral color management using interim connection spaces based on spectral decomposition

AbstractA 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.

Spectral Modeling of an n-Ink Printer via Thin Plate Spline Interpolation

Reducing metamerism is one of the main goals of spectral printing. There are three main steps in spectral printing to address:1. What is the printer gamut in spectral space (Printer Forward Model)2. Given an input spectrum, what is the closest point on the printer gamut to the input reflectance (Spectral Gamut Mapping)3. What ink density combination produces the given point on the gamut (Ink Separation)In this paper we address the first issue for printers with a large number of inks. The performance of the model is evaluated based on an 8-ink printer and is compared to the most commonly used technique. We show that the new model performs as accurately as the existing models but does not require as many as constraints to set up the model.

Spectral Gamut Mapping Framework based on Human Color Vision

Spectral Gamut Mapping Framework based on Human Color Vision

Spectral Gamut Mapping and Gamut Concavity

Spectral Gamut Mapping and Gamut Concavity

Spectral Gamut Mapping using LabPQR

Spectral color management requires inversion of printer spectral characterizations and necessarily involves the concept of spectral gamut mapping. A printer was spectrally characterized and the spectra were transformed to an interim connection space (ICS), a spectral description space with low dimensionality useful for building lookup tables (LUTs) of feasible sizes. LabPQR is the ICS used. It has separate dimensions describing colorimetry (CIELAB) and a spectrum's metameric black difference from a standard metamer (PQR). The relationship between digital value and LabPQR was inverted using a single stage objective function combining colorimetric and spectral criteria. The objective function's colorimetric criterion minimized CIEDE2000 under chosen conditions and its spectral criterion minimized Euclidian distance in PQR coordinates. A weight series was performed to find the optimal trade-off between colorimetric and spectral error. A 1:50 weighting ratio, CIEDE2000 to PQR difference, was deemed best. For the GretagMacbeth ColorChecker, the proposed single stage objective function showed equivalent levels of the performance to a full 31-dimensional unmodified spectra approach, resulting in an average RMS error of 4.18% and an average CIEDE2000 of 0.03. The single stage objective function for spectral gamut mapping using LabPQR proved to be promising for spectral reproduction.

Spectral Colorimetry using LabPQR: An Interim Connection Space

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.

Spectral Colorimetry Using LabPQR – An Interim Connection Space

A method of converting reflectance spectra to a convenient intermediate form introduced as LabPQR is used as an Interim Connection Space (ICS) within a spectral color management system. The LabPQR ICS makes use of a spectral encoding that explicitly incorporates colorimetry with additional dimensions that define spectral error corrections that allow an inverse transformation to approximate 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 characteristics. Several example transformations are demonstrated using various output devices with spectral gamut visualizations. Observations are made, and from these observations, a possible method of performing spectral gamut mapping is proposed.