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

Tal Frank, Jiayin Liu, Shani Gat, Oren Haik, Orel Bat Mor, Itamar Roth, Jan P. Allebach, Yitzhak Yitzhaky

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)5498-5512
Number of pages15
JournalIEEE Trans. Image Process.
Volume31
DOIs
StatePublished - 2022

Keywords

  • color halftoning
  • Halftone screen
  • aperiodic
  • clustered-dot halftone texture
  • direct binary search
  • radially averaged power spectrum
  • print quality
  • machine learning ,

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