Spectral prediction method based on the transformer neural network for high-fidelity color reproduction

Abstract

Color distortion often occurs during transmission and reproduction processes, and existing spectral prediction methods have the disadvantage of low prediction accuracy in halftone reproduction. Addressing this issue, this paper establishes a halftone dataset composed of four-color inks (CMYK) mixtures. Based on this, the transformer network is introduced to model and characterize the spectral features of mixed inks, and a forward color formulation prediction model and a reverse spectral prediction model combining halftone reproduction with spectral sequences are proposed, namely the spectrum-color transformer (SC-Former). Color reproduction quality assessment experiments are conducted using the dataset established in this paper and the international standard Ugra/Fogra Media Wedge V3.0 test set. The experimental results show that the SC-Former model outperforms traditional physical models and data-driven prediction models in terms of color reproduction effects and spectral prediction accuracy. This research contributes to the development of high-fidelity color reproduction techniques.