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Color space conversion via gamut-based color samples of printer

机译:通过基于色域的打印机颜色样本进行颜色空间转换

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摘要

Because many types of electronic imaging devices are now available, cross-media color reproduction technology has received widespread attention due to the need to provide accurate color stimuli for different devices. In the case of cross-media color reproduction between a monitor and a printer, RGB has to be converted into a de vice-independent color space in order to translate the color information between the two devices. Thereafter, gamut mapping is used to compensate for any gamut mismatch and device-independent colors have to be re-converted into output colors such as CMY control values for printing. For color conversion between device colors and device-independent colors, empirical representation using sample measurements is currently widely utilized. In the case of the printer, color samples are uniformly selected in the colorant space, printed as color patches, and then measured. However, because these color samples are not evenly distributed inside the printer gamut, the color conversion error is increased. Accordingly, this article introduces a color-sampling algorithm for a printer to reduce the error in color conversion, and the performance is analyzed via color conversion experiments using three conversion methods, regression, neural network, and interpolation. [References: 23]
机译:由于现在可以使用多种类型的电子成像设备,因此跨媒体色彩再现技术由于需要为不同的设备提供准确的色彩刺激而受到了广泛的关注。在监视器和打印机之间进行跨媒体色彩再现的情况下,必须将RGB转换为与设备无关的色彩空间,以便在两个设备之间转换色彩信息。此后,使用色域映射来补偿任何色域不匹配,并且必须将与设备无关的颜色重新转换为输出颜色,例如用于打印的CMY控制值。为了在设备颜色和与设备无关的颜色之间进行颜色转换,当前广泛使用使用样本测量的经验表示。对于打印机,在色浆空间中均匀选择颜色样本,将其打印为色块,然后进行测量。但是,由于这些颜色样本在打印机色域内分布不均匀,因此颜色转换错误会增加。因此,本文介绍了一种用于打印机的颜色采样算法,以减少颜色转换中的错误,并通过使用回归,神经网络和插值这三种转换方法的颜色转换实验对性能进行了分析。 [参考:23]

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