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 language | English |
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Pages (from-to) | 19780-19795 |
Number of pages | 16 |
Journal | IEEE Access |
Volume | 10 |
DOIs | |
State | Published - 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