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Volume: 33 | Article ID: art00006
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Banding defect detection and image quality classification
Yi Yang
Eric Maggard
Yousun Bang
Minki Cho
Jan P. Allebach
  DOI :  10.2352/ISSN.2470-1173.2021.16.COLOR-245  Published Online :  January 2021
Abstract

Banding has been regarded as one of the most severe defects affecting the overall image quality in the printing industry. There has been a lot of research on it, but most of them focused on uniform pages or specific test images. Aiming at detecting banding on customer’s content pages, this paper proposes a banding processing pipeline that can automatically detect banding, identify periodic and isolated banding, and estimate the periodic interval. In addition, based on the detected banding characteristics, the pipeline predicts the overall quality of printed customer’s content pages and obtains predictions similar to human perceptual assessment.

Journal Title : Electronic Imaging
Publisher Name : Society for Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151 USA
Subject Areas :
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 DOI 10.2352/ISSN.2470-1173.2021.16.COLOR-245
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  Cite this article 

Yi Yang, Eric Maggard, Yousun Bang, Minki Cho, Jan P. Allebach, "Banding defect detection and image quality classification"  in Proc. IS&T Int’l. Symp. on Electronic Imaging: Color Imaging XXVI: Displaying, Processing, Hardcopy, and Applications,  2021,  pp 245-1 - 245-9,  https://doi.org/10.2352/ISSN.2470-1173.2021.16.COLOR-245

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Copyright © Society for Imaging Science and Technology 2021
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA
10.2352/ISSN.2470-1173.2021.16.COLOR-245
2470-1173(20210118)2021:16L.2451;1-
ei_24701173_v2021n16_input/s7.xml
/ist/ei/2021/00002021/00000016/art00006
Articles
Banding defect detection and image quality classification
YangYi
MaggardEric
BangYousun
ChoMinki
AllebachJan P.
18012021
2021
16
245-1
245-9
2021
Banding has been regarded as one of the most severe defects affecting the overall image quality in the printing industry. There has been a lot of research on it, but most of them focused on uniform pages or specific test images. Aiming at detecting banding on customer’s content pages, this paper proposes a banding processing pipeline that can automatically detect banding, identify periodic and isolated banding, and estimate the periodic interval. In addition, based on the detected banding characteristics, the pipeline predicts the overall quality of printed customer’s content pages and obtains predictions similar to human perceptual assessment.
Image defect detectionImage quality assessmentImage classification