Colorimetric characterization of the wide-color-gamut camera using the multilayer artificial neural network
Colorimetric characterization of the wide-color-gamut camera using the multilayer artificial neural network
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DOI：
10.1364/josaa.481547
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发表时间：
2023-03-01
影响因子：
1.9
通讯作者：
Wu, Wenmin
中科院分区：
文献类型：
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作者：
LI, Yasheng;LI, Yumei;Wu, Wenmin
为了实现宽色域相机的色度表征，我们提出使用多层人工神经网络(ML-ANN)和误差反向传播算法来建模从相机的RGB空间到CIEXYZ标准的XYZ空间的颜色转换。本文介绍了最大似然神经网络的结构模型、正演计算模型、误差反向传播模型和训练策略。基于ColorChecker-SG块的光谱反射曲线和典型彩色相机RGB通道的光谱灵敏度函数，提出了一种用于ML-ANN训练和测试的宽色域样本的生成方法。同时，采用不同的多项式变换与最小二乘法进行了对比实验。实验结果表明，随着隐含层数目和各隐含层神经元数目的增加，训练和测试误差明显减小。具有最优隐含层的ML-ANN的平均训练误差和平均测试误差分别降低到0.69和0.84(CIELAB的色差)，远远优于包括四次多项式变换在内的所有多项式变换。(C)2023光学出版集团
In order to realize colorimetric characterization for the wide-color-gamut camera, we propose using the multilayer artificial neural network (ML-ANN) with the error-backpropagation algorithm, to model the color conver-sion from the RGB space of camera to the XYZ space of the CIEXYZ standard. In this paper, the architecture model, forward-calculation model, error-backpropagation model, and the training policy of the ML-ANN were introduced. Based on the spectral reflectance curves of the ColorChecker-SG blocks and the spectral sensitivity functions of the RGB channels of typical color cameras, the method of producing the wide-color-gamut samples for the training and testing of the ML-ANN was proposed. Meanwhile, the comparative experiment employing different polynomial transforms with the least-square method was conducted. The experimental results have shown that, with the increase of the hidden layers and the neurons in each hidden layer, the training and testing errors can be decreased obviously. The mean training errors and mean testing errors of the ML-ANN with optimal hidden layers have been decreased to 0.69 and 0.84 (color difference of CIELAB), respectively, which is much better than all the polynomial transforms, including quartic polynomial transform.(c) 2023 Optica Publishing Group