Spectral‐based color separation using linear regression iteration

Urban, Philipp; Grigat, Rolf-Rainer

doi:10.1002/col.20211

Cited by 24 publications

(24 citation statements)

References 12 publications

(2 reference statements)

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“…It aims to invert the "forward model" by certain constrained optimization algorithms and find suitable device control values for the reflectance spectrum to be printed. Over the past decade, several backward algorithms have been presented [7][8][9][10][11]. Taplin [11] made a comprehensive comparison among several standard numerical optimization methods.…”

Section: Introductionmentioning

confidence: 99%

Optimization of spectral printer modeling based on a modified cellular Yule–Nielsen spectral Neugebauer model

2014

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The study presented here optimizes several steps in the spectral printer modeling workflow based on a cellular Yule-Nielsen spectral Neugebauer (CYNSN) model. First, a printer subdividing method was developed that reduces the number of sub-models while maintaining the maximum device gamut. Second, the forward spectral prediction accuracy of the CYNSN model for each subspace of the printer was improved using back propagation artificial neural network (BPANN) estimated n values. Third, a sequential gamut judging method, which clearly reduced the complexity of the optimal sub-model and cell searching process during printer backward modeling, was proposed. After that, we further modified the use of the modeling color metric and comprehensively improved the spectral and perceptual accuracy of the spectral printer model. The experimental results show that the proposed optimization approaches provide obvious improvements in aspects of the modeling accuracy or efficiency for each of the corresponding steps, and an overall improvement of the optimized spectral printer modeling workflow was also demonstrated.

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Section: Introductionmentioning

confidence: 99%

Optimization of spectral printer modeling based on a modified cellular Yule–Nielsen spectral Neugebauer model

2014

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“…For the WF1 the colorant separation (CS) is performed for the 12 gamut mapped spectral reflectances using the linear regression iteration method (LRI) presented by [5]. From the 12 colorant combinations obtained we create 12 patches of 6 channels each and size 512 × 512 pixels.…”

Section: Wf1: Colorant Separation and Scalar Error Diffusion By Colormentioning

confidence: 99%

“…we want to reduce the metamerism. This task is performed by inverting the spectral Yule-Nielsen modified Neugebauer printer model [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Simple Comparison of Spectral Color Reproduction Workflows

Gerhardt

Hardeberg

2009

Image Analysis

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Abstract. In this article we compare two workflows for spectral color reproduction : colorant separation (CS) followed by halftoning by scalar error diffusion (SED) of its resulting multi-colorant channel image and a second workflow by spectral vector error diffusion (sVED). Identical filters are used in both SED and sVED to diffuse the error. Gamut mapping is performed as pre-processing and the reproductions are compared to the gamut mapped spectral data. The inverse spectral Yule-Nielsen modified Neugebauer (YNSN) model is used for the colorant separation. To bring the improvement of the YNSN model upon the regular Neugebauer model into the sVED halftoning the n factor is introduced in the sVED halftoning. The performances of both workflows are evaluated in term of spectral and color differences but also visually with the dot distributions obtained by the two halftoning techniques. Experimental results have shown close performances for the compared workflows in term of color and spectral differences but visually cleaner and more stable dot distributions obtained by sVED.

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“…[8][9][10][11][12][13][14][15][16] Usually, these works divide the ink set into smaller subsets and make the color separation simpler. When there are multiple choices between different ink subsets, they might select the solution that optimizes one or more print attributes.…”

Section: Introductionmentioning

confidence: 99%

Color Reproduction of Metallic-Ink Images

Babaei¹,

Hersch²

2016

jist

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Abstract. We study the color reproduction of full-color metallic-ink images. Full-color metallic-ink images are prints whose contributing colorants are exclusively made of colored metallic inks. Due to the presence of metallic particles, metallic inks show a metal-like luster. These particles are opaque and hide the underlying ink or substrate. In order to obtain predictable halftone colors, we need a juxtaposed halftoning method to create halftone dots of different colors side by side without overlapping. Juxtaposed halftoning invalidates many assumptions generally made for the color-reproduction workflow. For printing metallic-ink images, one needs a color-separation system creating surface coverages for the eight metallic inks that correspond to the eight Neugebauer primaries. For this purpose, we introduce a simple and fast method for N-color separation that relies either on Demichel's or on a variant of Kueppers' ink-to-colorant separations. Thanks to a unique set of ink-to-colorant formulas, pseudo-CMY ink values are separated into amounts of printable colorants. We also describe color-separation procedures that are able to optimize different properties of the resulting metallic-ink images. INTRODUCTIONA common element present in all printing applications is the color-reproduction workflow. A color-reproduction workflow converts an input image into the printer's command language. 1 Most existing reproduction workflows follow the steps shown in Figure 1. Briefly, input colors are first converted from a source color space, such as sRGB, into a device-independent color space such as CIELAB. 2 By performing gamut mapping, 3 the input colors are mapped into the colors of the usually narrower print gamut. Then, the color separation is carried out by converting the gamut-mapped printable colors into amounts of printer inks. The separated ink layers are then halftoned and printed.We can use a spectral prediction model for color separation and gamut mapping. The forward prediction model expresses the forward characterization. It determines the printer's color response to input control values, e.g. the amount of inks to be printed. By varying the amount of inks in the forward model, we can determine the color gamut of a printer. The spectral prediction model is also required in inverse mode for the color separation in order to deduce the amounts of inks that are needed to print a specific color.

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Spectral‐based color separation using linear regression iteration

Cited by 24 publications

References 12 publications

Optimization of spectral printer modeling based on a modified cellular Yule–Nielsen spectral Neugebauer model

Optimization of spectral printer modeling based on a modified cellular Yule–Nielsen spectral Neugebauer model

Simple Comparison of Spectral Color Reproduction Workflows

Color Reproduction of Metallic-Ink Images

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