摘要
桌面喷墨打印机是日常生活中重要的颜色再现设备,其颜色特性化包括两个主要部分:打印机的前向特性化,即建立与设备相关的RGB颜色空间到与设备无关的光谱或色度之间的转换;打印机的逆向特性化,即建立已知的反射光谱到RGB颜色空间的转换,以尽可能准确的再现颜色的光谱或色度。相对来说,逆向模型对于实际应用更有价值。
论文首先根据物理混色原理建立前向打印模型,分析了打印机内的墨水分色过程和介质上的呈色过程。对于呈色过程,分别采用基于简单点增益的YNSN模型和基于复杂点增益的改进型YNSN模型。并通过非线性优化求解分色过程中图像RGB对应的CMYK喷墨量,并建立两者间的多项式回归关系。论文还采用三维查找表法和局部多项式回归法实现打印机的前向颜色特性化。最后提出了一种六邻域局部空间算法,通过权重参数和幂因子的优化,进一步提高了算法精度。
在研究逆向打印模型时,首先采用单光源多项式回归法建立CIEXYZ到RGB空间的映射关系,实现了在本光源下的精确颜色再现,但其在不同光源下未能获得较好效果。为此,本论文最后提出了一种局部空间数值优化方法,通过参数的优化,进一步提高了逆向模型的算法精度,实现了多光谱图像的高保真打印。
For a desktop color printer, which is a typical output device, the characterization consists of forward and inverse models. The forward model seeks to convert the device-dependent control values (e.g., RGB) to the device-independent reflectance spectrum or colorimetric stimulus. The inverse model, which is more useful in color applications, transforms from a requested reflectance spectrum to the optimal control values that, when printed, could reproduce that spectrum or colorimetric stimulus with greatest possible accuracy.
First of all, establish the forward model, based on the principle of physical mixed color. The method investigates the printer color separation and medium color rendering processes, based on which, spectral reflectance can be predicted. The YNSN and enhanced YNSN models, which are based on simple and complicated dot gain respectively, are employed in the printer color separation process. As the color separation cannot be directly controlled, the colorimetric error objective function is designed. The mapping between RGB and CMYK is modeled using polynomial regression, and is resolved by nonlinear optimization. In the paper, we also use 3D-LUT and local polynomial regression to establish the forward model. Finally, six field local space algorithm is presented. Through the weight parameters and the optimization of power factor, the algorithm precision is improved.
For the inverse model, first establish the mapping between CIEXYZ and RGB with polynomial regression, which is under a single light. The algorithm has good colorimetric accuracy under the light and leads to bad results in a different light. To solve the problem, the method of RGB averaging is presented. Finally, a local space numerical optimization method is presented. Through the optimization of parameters, the algorithm precision is improved and the good accuracy of the print of multispectral image is realized.
引文
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