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Sensing and Actuation: A Case for Multidisciplinary Engineering Education Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Blur-Specific No-Reference Image Quality Assessment: A Classification and Review of Representative Methods

Authors : Dingquan Li, Tingting Jiang

Published in: The Proceedings of the International Conference on Sensing and Imaging

Publisher: Springer International Publishing

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Abstract

Blur is one of the most common distortions that affect image quality, and this work focuses on blur-specific no-reference image quality assessment (NR-IQA). Since various blur-specific NR-IQA methods have been proposed, we first give an overall classification of existing methods. Among all categories, we introduce 18 representative methods. Then, we conduct comparative experiments for the 13 representative methods with available codes on Gaussian blur images from TID2013 and realistic blur images from BID. Most existing methods have satisfactory performance on Gaussian blur images, but they fail to accurately estimate the image quality of realistic blur images. Therefore, it is needed to make further study in this field. At last, we provide discussions on realistic blur.

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Metadata
Title
Blur-Specific No-Reference Image Quality Assessment: A Classification and Review of Representative Methods
Authors
Dingquan Li
Tingting Jiang
Copyright Year
2019
Book
The Proceedings of the International Conference on Sensing and Imaging

Print ISBN: 978-3-319-91658-3

Electronic ISBN: 978-3-319-91659-0

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-91659-0_4