  Halftoning aims to reproduce a continuous-tone image with pixels whose
intensities are constrained to two discrete levels. This technique has been
deployed on every printer, and the majority of them adopt fast methods (e.g.,
ordered dithering, error diffusion) that fail to render structural details,
which determine halftone's quality. Other prior methods of pursuing visual
pleasure by searching for the optimal halftone solution, on the contrary,
suffer from their high computational cost. In this paper, we propose a fast and
structure-aware halftoning method via a data-driven approach. Specifically, we
formulate halftoning as a reinforcement learning problem, in which each binary
pixel's value is regarded as an action chosen by a virtual agent with a shared
fully convolutional neural network (CNN) policy. In the offline phase, an
effective gradient estimator is utilized to train the agents in producing
high-quality halftones in one action step. Then, halftones can be generated
online by one fast CNN inference. Besides, we propose a novel anisotropy
suppressing loss function, which brings the desirable blue-noise property.
Finally, we find that optimizing SSIM could result in holes in flat areas,
which can be avoided by weighting the metric with the contone's contrast map.
Experiments show that our framework can effectively train a light-weight CNN,
which is 15x faster than previous structure-aware methods, to generate
blue-noise halftones with satisfactory visual quality. We also present a
prototype of deep multitoning to demonstrate the extensibility of our method.
