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.

There have been many advances in printer model-based halftoning. However, virtually all of this work has focused on electrophotographic (EP) printers. The lack of comparable work for inkjet (IJ) printers may be due to the fact that IJ printers do render dots that have a nearly hard, ideal profile, and are much more stable than dots rendered by EP printers. However, as we shall see, IJ printers do exhibit dot displacement errors that are caused by misaligned nozzles in the printhead, and which appear random in nature. These error can cause significant artifacts in the printed image. The goal of this paper is to model the IJ printer, and then to incorporate this model into halftoning algorithms so that high print quality can be achieved. Here, we develop a stochastic printer model based on the dot statistics of the IJ printer.

A new color halftoning algorithm for very limited flat panel displays is proposed. We introduce a new method of color image halftoning that incorporates the human visual characteristics within the frame work of error diffusion to improve the quality of halftoned images. Moreover, by exploiting an adaptive processing technique which eliminates the estimation of the second-order statistics for input images, coefficients of the error diffusion filter are updated adaptively according to the characteristics of the input image. The image quality resulting from the proposed algorithm is measured by subjective evaluation and shows less artificial contouring and visual worm patterns.