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A Machine Learning Approach to Design of Aperiodic, Clustered-Dot Halftone Screens via Direct Binary Search
IEEE Transactions on Image Processing ( IF 10.6 ) Pub Date : 2022-08-11 , DOI: 10.1109/tip.2022.3196821
Tal Frank 1 , Jiayin Liu 2 , Shani Gat 1 , Oren Haik 1 , Orel Bat Mor 1 , Itamar Roth 1 , Jan Allebach 2 , Yitzhak Yitzhaky 3
Affiliation  

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:

通过直接二分搜索设计非周期性簇点半色调网屏的机器学习方法

由于其固有的稳定性和对莫尔伪影的抵抗力,非周期性、聚集点、半色调图案最近在使用激光、电子照相印刷机的连续色调图像的商业印刷中变得流行。使用多级、多通道、集群直接二分搜索 (MS-MP-CLU-DBS) 算法设计的半色调网屏可以产生具有非常高视觉质量的半色调图案。但是这些半色调图案的特性取决于三个输入参数,没有已知的公式可以选择它们的值来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,开发了两个将这三个参数作为输入的预测器。一个预测半色调图案的质量水平。另一个预测半色调图案的比例。为了提供训练这些预测器的基本事实信息,人类受试者查看了从 MS-MP-CLU-DBS 设计的屏幕生成的大量半色调色块,并将每个色块分配到四个质量级别之一。对于每个贴片,径向平均功率谱 (RAPS) 中峰值的位置被计算为图案的尺度或有效线频率的量度。实验结果证明了这两个预测器的准确性以及基于这些预测器的网屏设计程序生成单色和彩色高质量半色调图像的有效性。径向平均功率谱 (RAPS) 中峰值的位置被计算为模式的比例或有效线频率的量度。实验结果证明了这两个预测器的准确性以及基于这些预测器的网屏设计程序生成单色和彩色高质量半色调图像的有效性。径向平均功率谱 (RAPS) 中峰值的位置被计算为模式的比例或有效线频率的量度。实验结果证明了这两个预测器的准确性以及基于这些预测器的网屏设计程序生成单色和彩色高质量半色调图像的有效性。
更新日期:2022-08-11
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