A hybrid spectral prediction model for printed images based on whale-optimized deep neural network

In the process of color reproduction, the accurate prediction of color halftone images’ characteristics and the development of a spectral reflectance prediction model are pivotal for print image device characterization and quality control. Traditional models such as Murray-Davis, Clapper-Yule, Yule-Nielsen, and their modifications have been preferred for their high accuracy in color and spectral predictions. However, they overlook the role of black ink in CMYK printing, limiting their effectiveness in predicting the spectral properties of four-color inks and demonstrating notable in-accuracies in light color tones. A hybrid model combining a prior model based on physics with a deep neural network has been proposed. On the input side, the Neugebauer equation and the superposition of 4-color inks are considered, and the 4-color CMYK input is expanded to 16 Neugebauer primary colors. On the output side, the PCA dimensionality reduction algorithm extracts 7 principal components with a contribution of 99.99%. Finally, the Improved Whale Optimization Algorithm (IWOA) is employed to optimize the parameters of the deep neural network (DNN) model. The experimental results show that our model significantly outperforms traditional methods in reducing CIEDE2000 color differences, enabling the early prediction of spectral colors in printed images and improving print image quality. What is more, the proposed model does not need to take into account the effect of dot gain in the printing process.