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An improved parallel contrast-aware halftoning

机译:改进的并行对比度对比半色调

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digital image halftoning is a widely used technique. However, achieving high fidelity tone reproduction and structural preservation with low computational time cost remains a challenging problem. This paper presents a highly parallel algorithm to boost real-time application of serial structure-preserving error diffusion. The contrast-aware halftoning approach is one such technique with superior structure preservation, but it offers only a limited opportunity for graphics processing unit (GPU) acceleration. Our method integrates contrast-aware halftoning into a new parallelizable error-diffusion halftoning framework. To eliminate visually disturbing artifacts resulting from parallelization, we propose a novel multiple quantization model and space-filling curve to maintain tone consistency, blue-noise property, and structure consistency. Our GPU implementation on a commodity personal computer achieves a real-time performance for a moderately sized image. We demonstrate the high quality and performance of the proposed approach with a variety of examples, and provide comparisons with state-of-the-art methods.
机译:数字图像半色调是一种广泛使用的技术。但是,以低的计算时间成本实现高保真度的音调再现和结构保存仍然是一个具有挑战性的问题。本文提出了一种高度并行的算法,以提高保留串行结构的误差扩散的实时应用。对比度感知的半色调方法就是这样一种具有出色结构保留的技术,但是它为图形处理单元(GPU)加速提供的机会有限。我们的方法将对比度感知的半色调集成到新的可并行化的误差扩散半色调框架中。为了消除并行化产生的视觉干扰伪影,我们提出了一种新颖的多重量化模型和空间填充曲线,以保持色调一致性,蓝噪声特性和结构一致性。我们在商用个人计算机上的GPU实现可为中等大小的图像提供实时性能。我们通过各种示例展示了所提出方法的高质量和高性能,并与最新方法进行了比较。

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