提出了一种新的阴极射线管特性化的方法。该方法的特点是采用“视觉匹配”方法,在反射体表面色和自发光体之间映射一些色貌因素,但没有使用任何复杂的色貌模型。是一种考虑了一些色貌因素的阴极射线管特性化方法。由于该问题的个性因素较多,采用BP神经网络实现色空间的非线性映射。实验结果表明,只要阴极射线管被标定,在办公室环境下,该方法可以改进在不同的阴极射线管上重现的颜色。采用3-7-7-3简单的网络结构;分色相样本训练。训练样本平均色差可以达到3.07L*u*v*色差单位,测试样本平均色差可以达到4.55L*u*v*色差单位,小于阴极射线管的最大可接受色差,结果是令人满意的。这在电子商务和办公自动化方面有广泛的用途。

A new method of cathode-ray tube (CRT) monitors characterization is proposed. The features of this method are, it adopted a vision matching method to map some color appearance factors between self-luminous body and reflector surface colors, but without the complexity of using any color appearance model. It is a method that it has considered some color appearance factors. The neural networks were utilized to realize nonlinear mapping in color space. The experiment indicated that the method may improve the reproduction colors in different CRT when an arbitrary assigned color is to be displayed on a CRT screen, in office environment. Some simple network structure and small samples training method were adopted. The average color difference of training samples is 3.07 L*u*v* unit and that of testing samples is 4.55 L*u*v*. These results are smaller than the biggest acceptable color difference 10 L*u*v* unit. The results are satisfying. The method can be widely used by electronic-commerce and automation in office