Abstract
The purpose of this study is to reduce the ready time of offset printing by establishing a halftone dot prediction model (HDPM) based on BP neural network. The input data of HDPM is spectral reflectance data and the output data is dot area percentage of monochrome inks. To make sure the Mean Square Error (MSE) between the trained output data and the original data negligible, some experiments are performed. We obtained a set of results for dot area percentage of monochrome inks. The HDPM has stable performance, but it doesn’t work well under some special conditions, such as ink with black and dark tone. To improve the precision of HDPM, we increase the training samples of the corresponding area. Therefore, the prediction performance of HDPM has improved, and it does increase the efficiency of printing production in some levels.
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Acknowledgements
This study is funded by Shaanxi Provincial Key Laboratory of project-Printing image quality assessment based on human visual features (13JS082).
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Liu, M., Zheng, Y., Tang, Z., Wang, W. (2018). A Novel Halftone Dot Prediction Model Based on BP Neural Network. In: Zhao, P., Ouyang, Y., Xu, M., Yang, L., Ren, Y. (eds) Applied Sciences in Graphic Communication and Packaging. Lecture Notes in Electrical Engineering, vol 477. Springer, Singapore. https://doi.org/10.1007/978-981-10-7629-9_8
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DOI: https://doi.org/10.1007/978-981-10-7629-9_8
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