Constrained Acquisition of Ink Spreading Curves From Printed Color Images

Today's spectral reflection prediction models are able to predict the reflection spectra of printed color images with an accuracy as high as the reproduction variability allows. However, to calibrate such models, special uniform calibration patches need to be printed. These calibration patches use space and have to be removed from the final product. The present contribution shows how to deduce the ink spreading behavior of the color halftones from spectral reflectances acquired within printed color images. Image tiles of a color as uniform as possible are selected within the printed images. The ink spreading behavior is fitted by relying on the spectral reflectances of the selected image tiles. A relevance metric specifies the impact of each ink spreading curve on the selected image tiles. These relevance metrics are used to constrain the corresponding ink spreading curves. Experiments performed on an inkjet printer demonstrate that the new constraint-based calibration of the spectral reflection prediction model performs well when predicting color halftones significantly different from the selected image tiles. For some prints, the proposed image based model calibration is more accurate than a classical calibration