依据明度值对色块进行分区处理,按各区占总样本的百分比选取训练样本,采用BP 神经网络模型进行X-Rite 530 与SP60 测量数据的拟合。仿真结果及主观评价Z 得分显示,基于BP 神经网络的X-Rite 530 与SP60 台间差自适应修正模型优于三维空间拟合修正方法。提出的自适应修正模型为实现由低精度到高精度颜色测量仪器色度值的转换提供了理论依据,提高了印刷质量检测精度。

The measured patches were partitioned into different parts according to the lightness value. The training samples were selected according to the districts' percentage of total samples. BP neural network model was used to fit X-Rite 530's measured data with the SP60's. The simulation results and the subjective evaluation Z-score showed that BP neural network adaptive correction for the difference between X-Rite 530 and SP60 is better than 3D fitting algorithm. The purpose was to provide theoretical basis for realizing prediction of low-precision to high-precision color measurement instrument chroma values, and improve the accuracy of printing quality detection.