Prediction of gray balance spectral data in digital printing

Abstract

Now, the calculation method of gray balance data uses the parameters of density and chroma, and there is on gray balance data based on spectral reflectance. By the reflectivity in the visible wavelength range, the gray balance data can be predicted. The BP neural network is adopted to train, in which the spectral reflectance of the black ink which is of the same dot area value is adopted as neutral gray value, and the corresponding dot area value of cyan, magenta, and yellow inks can be predicted. In digital printing, gray balance is mainly affected by the paper and ink, so it is necessary to correct the calculated gray balance data. In the correction process, the black ink’s spectral data are corrected by the paper and ink’s spectral data. After correction, the error of gray balance is reduced. This study plays an important role in color control by gray balance data.