11 results
Now showing1 - 10 of 11
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Metamer density estimated color correctionColor correction is the transformation of response values of scanners or digital cameras into a device- independent color space. In general, the transformation is not unique due to different acquisition and viewing illuminants and non-satisfaction of the Luther-Ives condition by a majority of devices. In this paper we propose a method that approximates the optimal color correction in the sense of a minimal mean error. The method is based on a representative set of reflectance spectra that is used to calculate a special basic collection of device metameric blacks and an appropriate fundamental metamer for each sensor response. Combining the fundamental metamer and the basic collection results in a set of reflectances that follows the density distribution of metameric reflectances if calculated by Bayesian inference. Transforming only positive and bounded spectra of the set into an observer's perceptually uniform color space results in a point cloud that follows the density distribution of device metamers within the metamer mismatch space of acqcuisition system and human observer. The mean value of this set is selected for color correction, since this is the point with a minimal mean color distance to all other points in the cloud. We present the results of various simulation experiments considering different acquisition and viewing illuminants, sensor types, noise levels, and existing methods for comparison.Publicationtype: Journal Article32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Color correction by considering the distribution of metamers within the mismatch gamutPublicationtype: Conference Paper33 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Spectral-based color separation using linear regression iterationThe present article will introduce a very simple new method for spectral-based color separation. This method inverts a Yule-Nielsen modified spectral Neugebauer model, utilizing its affine multilinearity in the 1/n-space. By means of linear regression, a sequence of colorant combinations is constructed converging to a colorant combination that approximates the desired reflectance spectrum in the sense of the smallest RMS error. Each iteration step consists mainly of two simple matrix-vector multiplications. With the aid of various simulation experiments, investigations on the speed of convergence are conducted.Publicationtype: Journal Article34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Eine einfache Methode zur Invertierung des spektralen Yule-Nielsen modifizierten Neugebauer Modells(2005); Publicationtype: Conference Poster not in Proceedings25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Visualization and quantity estimation of mismatch gamuts of metamers viewed under different illuminantsPublicationtype: Conference Paper21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , The metamer boundary descriptor method for color correctionColor correction describes the transformation process between device response values, e.g., RGB, and CIE XYZ or CIE L*a*b* values, respectively. In every metamer color reproduction system this is the first color transformation after image acquisition. In general, this mapping is not unique because the spectral sensitivities of the majority of devices do not satisfy the Luther condition and the acquisition and viewing light sources typically have different power spectra. Hence, there exists a set of colors with different reflectance spectra which result in the same device response (device metamerism), but have different CIE XYZ tristimulus values for an observer under the viewing light source. In this publication we present a new method to determine this metamer subspace (which depends on the device response) in the viewing CIE L*a*b* space by calculating a Metamer Boundary Descriptor (MBD) matrix. This MBD matrix approximately describes the metamer subspace by storing its boundary points in every entry. By calculating the center of gravity of this MBD we obtain a good color choice in the sense of a small mean error. To calculate the entries of the MBD we use a priori knowledge about the physics of natural reflectance spectra and a linear programming technique. This method improves the performance of target- and regression-based methods especially for saturated colors. We present the results of simulation experiments including a comparison with existing methods. © 2005, IS&T - The Society for Imaging Science and Technology.Publicationtype: Journal Article27 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 31 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Gamut boundary determination by sampling an inverse printer modelIn this paper we present a novel method to calculate the gamut boundary of a color printing device using a sparse number of measured values. This method belongs to the SMGBD (Segment Maxima Gamut Boundary Descriptor) family. At first, we construct an inverse printer model based on a set of printed color patches. This model allows us to find the colorant values that have to be sent to the printer in order to obtain a given color appearance, or to show that such a colorant value does not exist. Then we construct a GBD (Gamut Boundary Descriptor) with the measured color patches, using the segment maxima method. After calculating the GBD every segment contains exactly one color value, which might not necessarily be part of the device gamut boundary. Therefore we sample along the line determined by the contained color value and the middle grey value (L, a, b) = (50, 0, 0) for every segment, using a bisection method based on the inverse printer model, and store the printable color value on the line with the largest distance from the middle grey value in the GBD. Due to this approach practical gamut boundaries can be calculated based on a sparse number of measured colors. Measurement results and a comparison with other methods are given in the text.Publicationtype: Conference Paper30