A Study on Halftoning Improvement for Low-Resolution Digital Print Engines with Machine Learning Methods

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

Research output: Contribution to journalArticlepeer-review

Abstract

As today's printing volume worldwide decreases, and most traditional printing engines are expensive non-digital devices (offset), the demand for a low-cost digital replacement is rapidly increasing. A main disadvantage of digital presses is the low-resolution capabilities, introducing a compromise in the print quality (PQ). A key factor of print quality is the halftoning algorithm. A very common halftoning method is amplitude modulation (AM) halftone screening, in which dots are placed on a repetitive lattice, varying in size as a function of the grey level. The main AM screen design PQ challenge for low-resolution devices is the quantization frequencies, a disturbing pattern that usually emerges when a screen is approximated to a rational angle due to low resolution. Fourier-based analysis is a classical rule-based method to filter out screens that suffer from visually disturbing quantization patterns. This work presents a new approach that tackles this challenge by incorporating machine learning with the classic Fourier-based approach. Particularly, we show that a binary decision tree classifier with a Fourier-based feature vector has an accuracy of 95% in identifying quantization-free screens compared to the classic rule-based method, which has an accuracy of 66%. We conclude by demonstrating the use of the screen classifier to design a quantization-free screen set. This is done by first applying the screen classifier to the entire screen pool, that is, the set of all possible screens for a given print engine, followed by a rosette zero-moiré offset-like screen design.

Original languageEnglish
Pages (from-to)19780-19795
Number of pages16
JournalIEEE Access
Volume10
DOIs
StatePublished - 1 Jan 2022

Keywords

  • AM screen
  • Halftoning
  • classification and regression tree (CART)
  • irregular screens
  • machine learning
  • moiré
  • print quality
  • quantization
  • regular screens

ASJC Scopus subject areas

  • Computer Science (all)
  • Materials Science (all)
  • Engineering (all)

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