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| Study of the Multi-Spectral Characterization Model for Inkjet Printing System and Its Application |
| LIANG Jing1, 2, HUANG Hao3, LIAN Yu-sheng4, NING Si-yu1, SUN Liang1 |
1. School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116000, China
2. State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310007, China
3. National Laboratory of Color Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
4. School of Printing and Packaging Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China |
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Abstract With the development of spectroscopy, spectral characteristics of the model of the printing system become a research focus. Characterization model based on spectral matching by direct prediction device color spectral reflectance data can effectively reduce mesmerism phenomenon, provide the conditions for the realization of high-fidelity printing. This article is based on model Yule-Nielsen Spectral Neugebauer amended, carried out research on the accuracy of the model twelve-color printing system spectral characteristics of the model and how to improve. This topic first by color measurement instruments and measurement conditions, the ink jet printer system stability and accuracy of the study, the feasibility of providing the basis for subsequent sample design and measurement. Then, the establishment of this research forward YNSN equipment for model experiments, based on the principles CIELAB color space uniformly distributed brightness value, design, and outputs a 1 331 test sample, color and characteristics of the model based on the establishment of spectrum through, Meanwhile, the extraction section samples do training samples, the established forward model for verification. Final show, feature-based model to predict high precision spectroscopy, has obvious advantages, but also through the Introduction of post-Yule-Nielsen correction parameter value of n, proven, can be further improved spectral prediction accuracy.
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Received: 2016-11-17
Accepted: 2017-06-18
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