目的 为实现 LCD显示器的光谱特征化， 提出一种基于子空间划分的BP神经网络结合PCA的光谱特征化模型。方法 对显示器色空间进行子空间划分， 在各子空间中进行模型的训练与检测。结果子空间划分后模型的色度和光谱精度， 较未进行子空间划分的模型有明显提高， PCA在不影响模型精度的同时， 降低了光谱维度， 提高了算法的运行效率。结论 该模型是一种高精度显示器特征化模型。

The aim of this study was to propose a spectral characterization model using BP neural network based on subspace partition and PCA so as to achieve spectral characterization of LCD. Color space of monitor was divided into subspaces. Training and testing of the model was processed in subspaces. Experimental results showed that model parameter optimization in the subspace was beneficial to the improvement of colorimetric and spectral accuracy of the model after subspace division. PCA can improve the operating efficiency of algorithm and reduce spectral dimension under the circumstances of maintaining accuracy. The experiment implies that this is a high-precise characteristic model for display.