Aperiodic, clustered-dot, halftone patterns have recently become popular for commercial printing of continuous-tone images with laser, electrophotographic presses, because of their inherent stability and resistance to moiré artifacts. Halftone screens designed using the multistage, multipass, clustered direct binary search (MS-MP-CLU-DBS) algorithm can yield halftone patterns with very high visual quality. But the characteristics of these halftone patterns depend on three input parameters for which there are no known formulas to choose their values to yield halftone patterns of a certain quality level and scale. Using machine learning methods, two predictors are developed that take as input these three parameters. One predicts the quality level of the halftone pattern. The other one predicts the scale of the halftone pattern. To provide ground truth information for training these predictors, human subjects viewed a large number of halftone patches generated from MS-MP-CLU-DBS-designed screens and assigned each patch to one of four quality levels. For each patch, the location of the peak in the radially averaged power spectrum (RAPS) is calculated as a measure of the scale or effective line frequency of the pattern. Experimental results demonstrate the accuracy of the two predictors and the effectiveness of screen design procedures based on these predictors to generate both monochrome and color high quality halftone images.

中文翻译：

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通过直接二分搜索设计非周期性簇点半色调屏幕的机器学习方法


非周期性、簇点、半色调图案最近在使用激光、电子照相印刷机进行连续色调图像的商业印刷中变得很流行，因为它们具有固有的稳定性和抗莫尔伪影的能力。使用多级、多通道、集群直接二分搜索 (MS-MP-CLU-DBS) 算法设计的半色调屏幕可以产生具有非常高视觉质量的半色调图案。但这些半色调图案的特性取决于三个输入参数，对于这三个输入参数，没有已知的公式来选择它们的值以产生一定质量水平和规模的半色调图案。使用机器学习方法，开发了两个预测器，将这三个参数作为输入。人们可以预测半色调图案的质量水平。另一种预测半色调图案的比例。为了提供用于训练这些预测器的真实信息，人类受试者查看了由 MS-MP-CLU-DBS 设计的屏幕生成的大量半色调补丁，并将每个补丁分配给四个质量级别之一。对于每个补丁，计算径向平均功率谱 (RAPS) 中峰值的位置，作为图案的尺度或有效线频率的度量。实验结果证明了两个预测器的准确性以及基于这些预测器的屏幕设计程序生成单色和彩色高质量半色调图像的有效性。