| 光谱学与光谱分析 |
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| Research on Assessment Methods of Spectral Reflectance Data Quality for Computer Color Matching |
| HE Cheng-dong1, WAN Xiao-xia1*, HUANG Xin-guo1,2,3, CHEN Hua-pei4, OU Li-guo4, ZHAO De-fang1 |
1. Department of Printing and Packaging，Wuhan University，Wuhan 430079，China
2. School of Packaging and Material Engineering，Hunan University of Technology，Zhuzhou 412007，China
3. Time Publishing and Media Co.，Ltd.，Hefei 230071，China
4. Changde Jinpeng Printing Co.，Ltd.，Changde 415000，China |
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Abstract Spectral reflectance data quality is important for computer color matching. There are two existing methods for evaluating the quality—spectral reflectance method and K/S method, which are too complex to apply. In this paper, 45°/0° and d/8° geometric conditions are used in the measurement of spectral reflectance of the offset ink samples printed on coated paper and silver-foiled paper while improvement on the geometric condition is made on the basis of the spectral reflectance method. Moreover, a new evaluation method—lightness and chromaticity comparative method is put forward, and comparison is made among the three methods. The results show that both 45°/0° and d/8° are feasible in the measurement of spectral reflectance of coated paper; however the former one cannot meet the requirement of spectral reflectance measurement of silver-foiled paper. In addition, as to d/8° Specular Component Included (SCI), when the silver-foiled paper is taken as the substrate, the reflectance of transparent white ink samples are smaller than that of other primary inks; and abnormal intersections appear in the curves of cyan and magenta ink respectively at the concentration of 60%, resulting in a poor spectra quality at high ink concentration; In the figure of lightness and chromaticity curves, there is significant divergence of the cyan and magenta ink curves from the referenced coated paper. In conclusion, the spectral reflectance of the transparent ink should be greater than or at least equal to other primary inks, and the maximum concentration of cyan and magenta should be limited; when the coated paper with good diffusion performance is taken as the reference, the comparative analysis is more intuitive than the two existing methods.
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Received: 2015-11-12
Accepted: 2016-03-10
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Corresponding Authors:
WAN Xiao-xia
E-mail: wan@whu.edu.cn
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[1] Lin J，Xu L，Zhang H Y. Color Research and Application，2013，39(6)：607.
[2] Choudhury A K R. Principles of Colour Appearance and Measurement，1st end. Cambridge：Woodhead Publishing，2014. 117.
[3] JIN Fu-jiang，TANG Yi-ping. Journal of Wuhan University of Technology·Information & Management Engineering，2008，30(1)：83.
[4] Saunderson J L. J. Opt. Soc. Am.，1942，32(12)：727.
[5] Huang X G，Wan X X，Liu Z. Color Research and Application，2013，38(2)：130.
[6] YUAN Kun，YAN Hui-min，JIN Shang-zhong，et al. Acta Optica Sinica，2013，33(11)：1.
[7] Bondioli F，Manfredini T，Romagnoli M，et al. Journal of the European Ceramic Society，2006，26(3)：311.
[8] Furferi R，Carfagni M. J. Applied Sci.，2010，10(18)：2108.
[9] Kubelka P，Munk F. Zeit Tech. Phys.，1931，12：593.
[10] WANG Gong-ming，LIU Zhi-yong. Spectroscopy and Spectral Analysis, 2015，35(6)：1682.
[11] Allen E. Journal of the Optical Society of America，1966，56(9)：1256. |
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