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

IEEE Access

Tal Frank¹  Shani Gat²  Orel Bat Mor²  Jan P. Allebach²  Yitzhak Yitzhaky³  Oren Haik⁴ 

[1] Department of Electro-Optics Engineering, School of Electrical and Computer Engineering, Ben Gurion University of the Negev, Be&x2019;Research and Development Department, HP Inc., Ness Ziona, Israel;School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA;er Sheva, Israel;

关键词: Halftoning;    moiré;    print quality;    AM screen;    regular screens;    irregular screens;   

DOI  :  10.1109/ACCESS.2022.3150925

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown