Spectral Image Prediction of Color Halftone Prints Based on Neugebauer Modified Spectral Reflection Image Model

Masayuki Ukishima1,2, Yoshinori Suzuki1, Norimichi
Tsumura1, Toshiya Nakaguchi1, Martti Mäkinen2,
Shinichi Inoue3, Jussi Parkkinen2
1Chiba University, Japan, 2University of Eastern Finland, Finland,
3Mitsubishi Paper Mills Ltd., Japan

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As the spectral prediction model for color halftone prints using the microscopic measurement, the conventional spectral reflection image (SRIM) is extended by introducing the concept of the conventional spectral Neugebaur Model, and a new production model, the Neugebauer modifies spectral reflection image model (NMSRIM), is proposed. Compared to the SRIM, the NMSRIM abstracts the spatio-spectral transmittance distribution of ink layer using the the limited number of base color functions and the spatial position function for each base color in order to efficiently predict the reflectance of color halftone prints from a small number of measurements. The NMSRIM separately analyzes the mechanical dot gain and the optical dot gain. The NSRIM can predict can predict not only the spectral reflectance but also the microscopic spatial distribution of reflectance. The spatial distribution of reflection of reflectance is related to the appearance of halftone prints. The methods to obtain the parameters of NMSRIM are also proposed. Several parameters are obtained by measurements and the others are obtained by computational estimations. To evaluate the validity of the NMSRIM, the spatio-spectral distrbution of reflectance printed with two inks, cyan and magenta (testing data) is predicted from the measurements of the halftones printed with one ink, the unprinted paper, and the solid prints of ink which are the cyan, magenta and blue (training data), where the blue corresponds to the combination of cyan and magenta inks. The spectral prediction accuracy was significant since the average and maximum values ΔE94 in all samples were 0.66 and 1.30, respectively. We also obtained the interesting results according to the spatial data.

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