| 光谱学与光谱分析 |
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| Colorimetric Characterization of LCD Based on Wavelength Partition Spectral Model |
| LIU Hao-xue1, CUI Gui-hua2*, HUANG Min1, WU Bing1, XU Yan-fang1, LUO Ming3 |
1. School of Printing & Packaging Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
2. School of Physics & Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China
3. Zhejiang University, Color and Image Technology Research Center, Hangzhou 310027, China |
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Abstract To establish a colorimetrical characterization model of LCDs, an experiment with EIZO CG19, IBM 19, DELL 19 and HP 19 LCDs was designed and carried out to test the interaction between RGB channels, and then to test the spectral additive property of LCDs. The RGB digital values of single channel and two channels were given and the corresponding tristimulus values were measured, then a chart was plotted and calculations were made to test the independency of RGB channels. The results showed that the interaction between channels was reasonably weak and spectral additivity property was held well. We also found that the relations between radiations and digital values at different wavelengths varied, that is, they were the functions of wavelength. A new calculation method based on piecewise spectral model, in which the relation between radiations and digital values was fitted by a cubic polynomial in each piece of wavelength with measured spectral radiation curves, was proposed and tested. The spectral radiation curves of RGB primaries with any digital values can be found out with only a few measurements and fitted cubic polynomial in this way and then any displayed color can be turned out by the spectral additivity property of primaries at given digital values. The algorithm of this method was discussed in detail in this paper. The computations showed that the proposed method was simple and the number of measurements needed was reduced greatly while keeping a very high computation precision. This method can be used as a colorimetrical characterization model.
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Received: 2013-02-06
Accepted: 2013-05-18
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Corresponding Authors:
CUI Gui-hua
E-mail: guihua.cui@gmail.com
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