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

    2 Scopus citations

    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

    • General Computer Science
    • General Materials Science
    • General Engineering

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