4-Row Serpentine Tone Dependent Fast Error Diffusion

Mao, Yafei; Abello, Lluis; Sarkar, Utpal; Ulichney, Robert; Allebach, Jan

doi:10.1109/icip.2018.8451627

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Halftoning techniques aim for reproducing continuous-tone images c∈[0,1]N with binary pixels h∈{0,1}N, where N denotes the number of pixels. In addition to classic approaches like ordered dithering, 1 – 4 error diffusion, 5 – 12 and search-based methods, 13 – 18 recently, deep learning-based solutions 19 – 25 are showing their abilities in rendering decent halftones with reversibility 21 or less computational complexity 23 . Specifically, convolutional neural networks (CNNs) are trained to project white Gaussian noise maps into halftone pixels conditioning on the continuous-tone image [illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Fast halftoning policy network with error feedback

Jiang

Xiong

Yin³

et al. 2023

J. Electron. Imag.

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Deep learning-based halftoning methods have demonstrated their abilities in dithering images with desired blue-noise properties. However, tons of parameters and computations come along with the 2D convolutional neural networks (CNNs), which hinder their practical usage, particularly in the cost-sensitive printing imaging system equipped with low-end computing devices. We devise a fast halftoning policy network to alleviate the problem. Motivated by classic error diffusion algorithms, we introduce an error feedback mechanism to model the dependencies between halftone lines. Consequently, the problem collapses to 1D deep halftoning within each line, which simplifies the learning task. Furthermore, a very simple convolutional layer is applied to capture the texture details from the continuous-tone image. We find that the proposed halftoning model can stably converge under the reinforcement learning-based framework. Experiments show that our model achieves a 10.6× speedup with only 1.7% parameters compared to a previous 2D CNN-based work while maintaining similar halftone quality. We have also conducted experiments on a digital signal processor to demonstrate how the proposed model saves expensive off-chip memory accesses and improves processing throughput in real scenarios.

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