光谱学与光谱分析, 2018, 38 (6): 1975, 网络出版: 2018-06-29   

基于LabPQR降维的多色打印机光谱特征化模型研究

Study on the Spectral Characterization Model of Multi-Color Printer Based on LabPQR Dimension Reduction
作者单位
1 上海理工大学出版印刷与艺术设计学院, 上海 200093
2 上海出版印刷高等专科学校, 上海 200093
摘要
研究多色打印机的光谱特征化, 提出了一种基于降维的光谱特征化模型, 保证了多色打印机颜色转换的精度, 同时也提高了特征化的运行效率。 该模型结合颜色分区理论和LabPQR非线性降维方法, 首先将高维光谱数据降低至LabPQR六维空间中, 然后通过胞元搜索算法查找目标颜色所属的胞元空间, 最后利用反向四面体插值算法对目标的LabPQR值进行计算, 得到打印机最终的通道信号输出值。 检测颜色样本的实验数据表明, 正向模型和反向模型的平均色差达到0.714和1.016 NBS, 模型的运行时间为2.03和9.05 s。 新算法能够实现多色打印机光谱数据与通道信号值间的准确转换。
Abstract
A spectral characterization model based on dimension reduction is proposed to guarantee the precision of color conversion of the multi-color printer and improve its operation efficiency. Color space division theory and LabPQR nonlinear dimensionality reduction method are adopted in the new model. Moreover, the forward model is created based on Lookup Table, and the backward model is created through cell search algorithm and inverse tetrahedral interpolation algorithm. Firstly, the high dimensional spectral reflectance is reduced to six dimensional space values (LabPQR). Then the cell space of the target color is searched using cell search algorithm. Finally, the LabPQR values of the target color are calculated with the inverse tetrahedral interpolation algorithm, and the output values of the multi-color printer channel signals are obtained. The experimental data of the tested color samples shows that the average color difference of the forward model and the backward model are 0.714 NBS and 1.016 NBS respectively, and the corresponding running time of the models are 2.03 and 9.05 s. New algorithm could realize the accurate inter-conversion between the spectral reflectance of a multi-color printer and its channel-signal values.

姜中敏, 孔玲君, 聂鹏, 于海琦. 基于LabPQR降维的多色打印机光谱特征化模型研究[J]. 光谱学与光谱分析, 2018, 38(6): 1975. JIANG Zhong-min, KONG Ling-jun, NIE Peng, YU Hai-qi. Study on the Spectral Characterization Model of Multi-Color Printer Based on LabPQR Dimension Reduction[J]. Spectroscopy and Spectral Analysis, 2018, 38(6): 1975.

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