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

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 i...
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Gespeichert in:

Autor*in:	Tal Frank [verfasserIn] Oren Haik [verfasserIn] Shani Gat [verfasserIn] Orel Bat Mor [verfasserIn] Jan P. Allebach [verfasserIn] Yitzhak Yitzhaky [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Halftoning moiré print quality AM screen regular screens irregular screens

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 19780-19795
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:19780-19795

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3150925

Katalog-ID:	DOAJ018309968

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520			\|a 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.
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spelling	10.1109/ACCESS.2022.3150925 doi (DE-627)DOAJ018309968 (DE-599)DOAJae2cf63220ff4fbda65002c4150fd5b8 DE-627 ger DE-627 rakwb eng TK1-9971 Tal Frank verfasserin aut A Study on Halftoning Improvement for Low-Resolution Digital Print Engines With Machine Learning Methods 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Halftoning moiré print quality AM screen regular screens irregular screens Electrical engineering. Electronics. Nuclear engineering Oren Haik verfasserin aut Shani Gat verfasserin aut Orel Bat Mor verfasserin aut Jan P. Allebach verfasserin aut Yitzhak Yitzhaky verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 19780-19795 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:19780-19795 https://doi.org/10.1109/ACCESS.2022.3150925 kostenfrei https://doaj.org/article/ae2cf63220ff4fbda65002c4150fd5b8 kostenfrei https://ieeexplore.ieee.org/document/9709797/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 19780-19795
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allfieldsGer	10.1109/ACCESS.2022.3150925 doi (DE-627)DOAJ018309968 (DE-599)DOAJae2cf63220ff4fbda65002c4150fd5b8 DE-627 ger DE-627 rakwb eng TK1-9971 Tal Frank verfasserin aut A Study on Halftoning Improvement for Low-Resolution Digital Print Engines With Machine Learning Methods 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Halftoning moiré print quality AM screen regular screens irregular screens Electrical engineering. Electronics. Nuclear engineering Oren Haik verfasserin aut Shani Gat verfasserin aut Orel Bat Mor verfasserin aut Jan P. Allebach verfasserin aut Yitzhak Yitzhaky verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 19780-19795 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:19780-19795 https://doi.org/10.1109/ACCESS.2022.3150925 kostenfrei https://doaj.org/article/ae2cf63220ff4fbda65002c4150fd5b8 kostenfrei https://ieeexplore.ieee.org/document/9709797/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 19780-19795
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callnumber-first	T - Technology
author	Tal Frank
spellingShingle	Tal Frank misc TK1-9971 misc Halftoning misc moiré misc print quality misc AM screen misc regular screens misc irregular screens misc Electrical engineering. Electronics. Nuclear engineering A Study on Halftoning Improvement for Low-Resolution Digital Print Engines With Machine Learning Methods
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topic_title	TK1-9971 A Study on Halftoning Improvement for Low-Resolution Digital Print Engines With Machine Learning Methods Halftoning moiré print quality AM screen regular screens irregular screens
topic	misc TK1-9971 misc Halftoning misc moiré misc print quality misc AM screen misc regular screens misc irregular screens misc Electrical engineering. Electronics. Nuclear engineering
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title	A Study on Halftoning Improvement for Low-Resolution Digital Print Engines With Machine Learning Methods
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title_auth	A Study on Halftoning Improvement for Low-Resolution Digital Print Engines With Machine Learning Methods
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	A Study on Halftoning Improvement for Low-Resolution Digital Print Engines With Machine Learning Methods
url	https://doi.org/10.1109/ACCESS.2022.3150925 https://doaj.org/article/ae2cf63220ff4fbda65002c4150fd5b8 https://ieeexplore.ieee.org/document/9709797/ https://doaj.org/toc/2169-3536
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author2	Oren Haik Shani Gat Orel Bat Mor Jan P. Allebach Yitzhak Yitzhaky
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