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Stochastic Floyd-Steinberg dithering on GPU: image quality and processing time improved

机译：随机弗洛伊德 - Steinberg抖动GPU：图像质量和加工时间得到改善

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Error diffusion dithering is a technique that is used to represent a grey-scale image in a format usable by a printer. At every step, an algorithm converts the grey-scale value of a pixel to a new value within the allowed ones, generating a conversion error. To achieve the effect of continuous-tone illusion, the error is distributed to the neighboring pixels. Among the existent algorithms, the most commonly used is Floyd-Steinberg. However, this algorithm suffers two issues: artifacts and slowness. Regarding artifacts, those are textures that can appear after the image elaboration, making it visually different from the original one. In order to avoid this effect, we will use a stochastic version of Floyd-Steinberg algorithm. To evaluate the results, we will apply the Weighted Signal to Noise Ratio (WSNR), a visual-based model to account for perceptivity of dithered textures. This filter has a low-pass characteristic and, in particular, it uses a Contrast Sensitivity Function to evaluate the similarity between the original image and the final image. Our claim is that the new stochastic algorithm is better suited for both the WSNR measure and the visual analysis. Secondly, we will face slowness: we will describe a parallel version of Floyd-Steinberg algorithm that will exploit GPU (Graphics Processing Unit), drastically reducing the spent time. Specifically, we noticed that the serial version computational time increases quadratically with the input size, while the parallel version one increases linearly. Both the image quality and the computational performance of the parallel algorithm are evaluated on several large-scale images.
机译：误差扩散抖动是一种技术，用于以打印机可用的格式表示灰度图像。在每个步骤中，算法将像素的灰度值转换为允许的允许的新值，生成转换误差。为了实现连续色调错觉的效果，误差被分发到相邻像素。在存在的算法中，最常用的是Floyd-Steinberg。然而，该算法遭受了两个问题：文物和慢化。关于工件，这些是在图像阐述之后出现的纹理，使其与原始图像不同。为了避免这种效果，我们将使用弗洛伊德 - Steinberg算法的随机版本。为了评估结果，我们将加权信号应用于噪声比（WSNR），基于视觉的模型，以解释抖动纹理的感知。该滤波器具有低通特性，并且特别地，它使用对比度灵敏度函数来评估原始图像和最终图像之间的相似性。我们的索赔是，新的随机算法更适合WSNR测量和视觉分析。其次，我们将面临缓慢：我们将描述一个并行版本的Floyd-Steinberg算法，它将利用GPU（图形处理单元），大大减少了花费时间。具体而言，我们注意到，串行版本的计算时间随着输入大小如二次数增加，而并行版本一个线性增加。在几个大规模图像上评估并行算法的图像质量和计算性能。

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