Adaptive characterization method for desktop color printers

Shen, Hui Liang; Zheng, Zhi; Jin, Chong; Du, Xin; Shao, Si Jie; Xin, John Haozhong

doi:10.1117/1.jei.22.2.023012

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…As shown in Eq. [6], the CYNSN model is actually the most widely used predication model among the several spectral Neugebauer models:…”

Section: The Problems Within Traditional Spectral Neugebauer Equationsmentioning

confidence: 99%

“…[2] This paper mainly focuses on the former process. Many mathematical models have been employed during color characterization, such as the spectral Neugebauer equations, [3,4] Kubelka-Munk model, [5] polynomial functions based on least-squares, [6,7] artificial neural networks (ANNs), [8,9] 3D interpolation techniques, [10][11][12][13] and so on. Spectral Neugebauer equations are established based on subtractive coloring principle and widely used for color characterization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modified Spectral Neugebauer Model for Printer Characterization

Sun

Liu

Zhou

et al. 2015

Spectroscopy Letters

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Spectral Neugebauer model is widely used for spectral reflectance prediction during printer characterization. However, several factors reduce the predication precision. Thus, an improved cellular Yule-Nielson spectral Neugebauer (CYNSN) model is proposed, which modifies the traditional spectral Neugebauer model in three main aspects: (1) First, in order to adjust the nonlinearities between the predicated and measured spectral reflectance, an iteratively calculated Yule-Nielson exponent is added to the reflectance values within the Neugebauer equations.(2) Second, the quantity of Neugebauer primaries is increased by dividing the CMY colorant space into 4 3 uniform cellular subdomains. (3) Third, the mapping functions are developed to map the area coverages' theoretical values to their effective values within the subdomains, and the mapped values highly improve the matching degree of the predicated and measured reflectance values. In the experiment, four related spectral Neugebauer models are employed during printer characterization, which are the traditional spectral Neugebauer model, Yule-Nielson spectral Neugebauer (YNSN) model, traditional CYNSN model, and the modified CYNSN model, respectively. And the experimental results show the modified CYNSN model yields a significant improvement over the other spectral Neugebauer models, by comparing the characterization errors in the form of colorimetry and spectroscopy.

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“…As shown in Eq. [6], the CYNSN model is actually the most widely used predication model among the several spectral Neugebauer models:…”

Section: The Problems Within Traditional Spectral Neugebauer Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modified Spectral Neugebauer Model for Printer Characterization

Sun

Liu

Zhou

et al. 2015

Spectroscopy Letters

View full text Add to dashboard Cite

show abstract

“…In the digital printing process, reliable color reproduction is always highly desired and printer color characterization to make connection between device color input to printer (RGB or CMYK) and output device independent color space (CIE XYZ or CIELAB) is an essential step [3]. For conventional color printer, various mathematical models have been developed and acknowledged to be effective, such as 3D look-up tables [4], least-squares based polynomial function [5], empirical techniques based on principal component analysis [6], the artificial neural network [7], etc. Most of those models focused on simple color transformations between RGB and CIE XYZ or CIELAB color spaces.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Color Characterization Methods for 3D Printer

He,

Xiao,

Pointer

et al. 2023

lim

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In this study, the third order polynomial regression (PR) and deep neural networks (DNN) were used to perform color characterization from CMYK to CIELAB color space, based on a dataset consisting of 2016 color samples which were produced using a Stratasys J750 3D color printer. Five output variables including CIE XYZ, the logarithm of CIE XYZ, CIELAB, spectra reflectance and the principal components of spectra were compared for the performance of printer color characterization. The 10-fold cross validation was used to evaluate the accuracy of the models developed using different approaches, and CIELAB color differences were calculated with D65 illuminant. In addition, the effect of different training data sizes on predictive accuracy was investigated. The results showed that the DNN method produced much smaller color differences than the PR method, but it is highly dependent on the amount of training data. In addition, the logarithm of CIE XYZ as the output provided higher accuracy than CIE XYZ.

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“…Many mathematical models have been adopted for forward modelling, which includes a least-squares based polynomial function [22], the Kubelka-Munk model [23], the artificial neural network (ANN) [24], the spectral Neugebauer model [25], etc. Among these models, the cellular Yule-Nielsen spectral Neugebauer (CYNSN) model, together with its variants, is perhaps the most preeminent model due to its intuition and precision [19,20,[26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Spectral Characterisation of a CMYK Printer with Embedded CMY Printer Modelling

et al. 2019

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In the digital printing process, reliable colour reproduction is commonly achieved by printer characterisation, which defines the correspondence between the input device control values and the output colour information. The cellular Yule–Nielsen spectral Neugebauer model, together with its variants, is widely adopted in this topic because of its superb colorimetric and spectral accuracy. However, it seems that current studies have neglected an inconspicuous defect in such models when characterising printers equipped with black ink. That is, the cellular structure of these models overemphasises the sampling for dark-tone colours, and thus leads to relatively large errors in light tones. In this paper, taking a CMYK printer as an example, a simple and effective solution is proposed with no need of extra sampling. With the aid of a newly built cellular spectral Neugebauer model for the embedded CMY printer, this approach optimises the printer characterisation for light tones, slightly improves the precision for middle tones while it maintains the accuracy for dark tones. The performance of the proposed method was evaluated with regard to three different kinds of substrates and the experimental results validated its improvement in spectral printer characterisation.

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Adaptive characterization method for desktop color printers

Cited by 4 publications

References 27 publications

Modified Spectral Neugebauer Model for Printer Characterization

Modified Spectral Neugebauer Model for Printer Characterization

Investigation on Color Characterization Methods for 3D Printer

Optimizing the Spectral Characterisation of a CMYK Printer with Embedded CMY Printer Modelling

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