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Research on Color Space Conversion Model from CMYK to CIE-LAB Based on GRNN

机译:基于GRNN的CMYK与CIE-LAB的颜色空间转换模型研究

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In order to reproduce color information accurately in cross media transmission, a color space conversion model from CMYK color space to CIE-LAB based on generalized regression neural network (GRNN) was proposed. According to the structure and mathematical model of GRNN neural network, the CMYK-LAB color space conversion model was established. By training sample and comparing the mean square error of the sample data, the distribution coefficients were determined, and CMYK- LAB color space conversion model based on GRNN was eventually obtained and the accuracy was tested. According to these data, sample data and test data was determined. The results showed that color space conversion from CMYK to CIE-LAB on GRNN had faster conversion speed and accuracy compared with the color space conversion method based on BP neural network, the demand on printing industry can be met. Color space conversion
机译:为了在跨媒体传输中精确地再现颜色信息,提出了基于广义回归神经网络(GRNN)的CMYK颜色空间的颜色空间转换模型。根据GRNN神经网络的结构和数学模型,建立了CMYK-LAB色彩空间转换模型。通过训练样本并比较样本数据的均方误差,确定了分配系数,并最终获得了基于GRNN的CMYK- Lab色彩空间转换模型,并测试精度。根据这些数据,确定样本数据和测试数据。结果表明,与基于BP神经网络的颜色空间转换方法相比,CMYK对CIE-LAB的颜色空间转换具有更快的转换速度和准确性,可以满足印刷行业的需求。颜色空间转换

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