Inkjet printer model-based halftoning

Abstract

The quality of halftone prints produced by inkjet (IJ) printers can be limited by random dot-placement errors. While a large literature addresses model-based halftoning for electrophotographic printers, little work has been done on model-based halftoning for IJ printers. In this paper, we propose model-based approaches to both iterative least-squares halftoning and tone-dependent error diffusion (TDED). The particular approach to iterative least-squares halftoning that we use is direct binary search (DBS). For DBS, we use a stochastic model for the equivalent gray-scale image, based on measured dot statistics of printed IJ halftone patterns. For TDED, we train the tone-dependent weights and thresholds to mimic the spectrum of halftone textures generated by model-based DBS. We do this under a metric that enforces both the correct radially averaged spectral profile and angular symmetry at each radial frequency. Experimental results generated with simulated printers and a real printer show that both IJ model-based DBS and IJ model-based TDED very effectively suppress IJ printer-induced artifacts.