In halftone-driven imaging pipelines focus is often placed on halftone pattern design as the main contributor to overall output quality. However, for sequential or cumulative imaging technologies, such as multi-pass printing, an important element is also pattern partitioning - how the overall halftone pattern is divided among the different partial imaging events such as printing passes. Partitioning is usually designed agnostically of the halftone pattern, making it impossible to optimize for the joint effect of halftone and partitioning. However, even a good halftone pattern coupled with a good partitioning scheme does not guarantee well partitioned halftones and can impact image quality attributes. In this paper a novel approach called PARAOMASKING is presented that benefits from the pattern-determinism of PARAWACS halftoning and proposes a partitioning scheme for multi-pass printing such that optimality is also obtained for partitioned halftones. Results - both digital and printed - show how it can lead to significant improvements in partial pattern quality and overall pattern quality. Consequently, output attributes such as grain, coalescence and pattern robustness are improved. The focus here is on blue-noise pattern preservation but the approach can also be extended to other objectives, e.g., maximizing per-pass clustering.

A novel statistical ink drop displacement (IDD) printer model for the direct binary search (DBS) halftoning algorithm is proposed. It is intended primarily for pagewide inkjet printers that exhibit dot displacement errors. The tabular approach in the literature predicts the gray value of a printed pixel based on the halftone pattern in some neighborhood of that pixel. However, memory retrieval time and the complexity of memory requirements hamper its feasibility in printers that have a very large number of nozzles and produce ink drops that affect a large neighborhood. To avoid this problem, our IDD model embodies dot displacements by moving each perceived ink drop in the image from its nominal location to its actual location, rather than manipulating the average gray values. This enables DBS to directly compute the appearance of the final printout without retrieving values from a table. In so doing, the memory issue is eliminated and the computation efficiency is enhanced. The deterministic cost function of DBS is replaced by the expectation over the ensemble of the displacements for the proposed model such that the statistical behavior of the ink drops is accounted for. Experimental results show significant improvement in the quality of the printed image over the original DBS. Besides, the image quality obtained by the proposed approach appears to be slightly better than that obtained by the tabular approach.

Error diffusion is a popular technique widely used in desktop printers, especially inkjet printers. However, since the conventional error diffusion is computed in raster order, it produces worm artifacts in the highlights and shadows. In addition, as a serial algorithm, it limits the efficiency and flexibility of hardware implementations. To address these two issues, we propose a novel serpentine based error diffusion algorithm that uses tone dependent error weights and thresholds. We also propose an expanded error weight location matrix to improve the halftone quality in the extreme tones. With this new algorithm, we achieve better halftones comparing to the original tone dependent fast error diffusion, especially in the quarter tones.