Color Space Conversion Model From CMYK to CIELab Based on Stacking Ensemble Learning

This paper develops a method based on a stacking ensemble learning model to achieve more accurate conversion from CMYK colors to LAB colors. The model employs tetrahedral interpolation, radial basis function (RBF) interpolation, and KAN as base learners, with linear regression as the meta-learner. Our findings show that the stacking-based model outperforms single models in accuracy for color conversion. In the empirical study, color blocks were printed and the collected data was measured to train and validate the stacking ensemble learning model. The results show that the stacking-based model achieves superior accuracy in color space conversion tasks. This research has substantial practical implications for enhancing color management technology in the printing industry.