A Machine Learning Approach to Design of Aperiodic, Clustered-Dot Halftone Screens via Direct Binary Search

<p>This dataset contains two parts: one has halftone patches that were used to predict the quality level and scale of halftone patterns using machine learning methods, and to allow others to perform their own ground truth assessment of halftone image quality. The second part contains full versions of the halftone images so viewers can zoom in to see the details since in the paper we only showed a crop due to the space limitation. </p>

<p>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 moire ́ 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.</p>