Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
The Proceedings of the International Conference on Sensing and Imaging (ICSI 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 506))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bahrami K, Kot AC (2014) A fast approach for no-reference image sharpness assessment based on maximum local variation. IEEE Sig Process Lett 21(6):751–755

    Article  Google Scholar 

  2. Bahrami K, Kot AC (2016) Efficient image sharpness assessment based on content aware total variation. IEEE Trans Multimed 18(8):1568–1578

    Article  Google Scholar 

  3. Bong DBL, Khoo BE (2014) Blind image blur assessment by using valid reblur range and histogram shape difference. Sig Process Image Commun 29(6):699–710

    Article  Google Scholar 

  4. Ciancio A, da Costa ALNT, da Silva EAB, Said A, Samadani R, Obrador P (2011) No-reference blur assessment of digital pictures based on multifeature classifiers. IEEE Trans Image Process 20(1):64–75

    Article  MathSciNet  Google Scholar 

  5. Feichtenhofer C, Fassold H, Schallauer P (2013) A perceptual image sharpness metric based on local edge gradient analysis. IEEE Sig Process Lett 20(4):379–382

    Article  Google Scholar 

  6. Ferzli R, Karam LJ (2009) A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Trans Image Process 18(4):717–728

    Article  MathSciNet  Google Scholar 

  7. Gu K, Zhai G, Lin W, Yang X, Zhang W (2015) No-reference image sharpness assessment in autoregressive parameter space. IEEE Trans Image Process 24(10):3218–3231

    Article  MathSciNet  Google Scholar 

  8. Guan J, Zhang W, Gu J, Ren H (2015) No-reference blur assessment based on edge modeling. J Vis Commun Image Represent 29:1–7

    Article  Google Scholar 

  9. Hassen R, Wang Z, Salama MMA (2013) Image sharpness assessment based on local phase coherence. IEEE Trans Image Process 22(7):2798–2810

    Article  Google Scholar 

  10. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501

    Article  Google Scholar 

  11. Kang L, Ye P, Li Y, Doermann D (2014) Convolutional neural networks for no-reference image quality assessment. In: IEEE conference on computer vision and pattern recognition, pp 1733–1740. IEEE

    Google Scholar 

  12. Leclaire A, Moisan L (2015) No-reference image quality assessment and blind deblurring with sharpness metrics exploiting Fourier phase information. J Math Imaging Vis 52(1):145–172

    Article  MathSciNet  Google Scholar 

  13. Li L, Lin W, Wang X, Yang G, Bahrami K, Kot AC (2016) No-reference image blur assessment based on discrete orthogonal moments. IEEE Trans Cybern 46(1):39–50

    Article  Google Scholar 

  14. Li L, Wu D, Wu J, Li H, Lin W, Kot AC (2016) Image sharpness assessment by sparse representation. IEEE Trans Multimed 18(6):1085–1097

    Article  Google Scholar 

  15. Li L, Xia W, Lin W, Fang Y, Wang S (2017) No-reference and robust image sharpness evaluation based on multi-scale spatial and spectral features. IEEE Trans Multimed 19(5):1030–1040

    Article  Google Scholar 

  16. Marziliano P, Dufaux F, Winkler S, Ebrahimi T (2004) Perceptual blur and ringing metrics: application to JPEG2000. Sig Process Image Commun 19(2):163–172

    Article  Google Scholar 

  17. Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–4708

    Article  MathSciNet  Google Scholar 

  18. Mukundan R, Ong S, Lee PA (2001) Image analysis by Tchebichef moments. IEEE Trans Image Process 10(9):1357–1364

    Article  MathSciNet  Google Scholar 

  19. Narvekar ND, Karam LJ (2011) A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Trans Image Process 20(9):2678–2683

    Article  MathSciNet  Google Scholar 

  20. Ponomarenko N, Jin L, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F, Kuo CCJ (2015) Image database TID2013: peculiarities, results and perspectives. Sig Process Image Commun 30:57–77

    Article  Google Scholar 

  21. Sang Q, Qi H, Wu X, Li C, Bovik AC (2014) No-reference image blur index based on singular value curve. J Vis Commun Image Represent 25(7):1625–1630

    Article  Google Scholar 

  22. Sheikh HR, Sabir MF, Bovik AC (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process 15(11):3440–3451

    Article  Google Scholar 

  23. VQEG (2000) Final report from the Video Quality Experts Group on the validation of objective models of video quality assessment. Video Quality Experts Group. http://vqeg.org/

  24. Vu PV, Chandler DM (2012) A fast wavelet-based algorithm for global and local image sharpness estimation. IEEE Signal Process Lett 19(7):423–426

    Article  Google Scholar 

  25. Vu CT, Phan TD, Chandler DM (2012) S 3: a spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans Image Process 21(3):934–945

    Article  MathSciNet  Google Scholar 

  26. Wang Z, Simoncelli EP (2003) Local phase coherence and the perception of blur. In: Advances in neural information processing systems, pp 1435–1442

    Google Scholar 

  27. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  28. Wang S, Deng C, Zhao B, Huang GB, Wang B (2016) Gradient-based no-reference image blur assessment using extreme learning machine. Neurocomputing 174:310–321

    Article  Google Scholar 

  29. Yu S, Wu S, Wang L, Jiang F, Xie Y, Li L (2017) A shallow convolutional neural network for blind image sharpness assessment. PloS One 12(5):e0176632

    Article  Google Scholar 

  30. Zhai G, Wu X, Yang X, Lin W, Zhang W (2012) A psychovisual quality metric in free-energy principle. IEEE Trans Image Process 21(1):41–52

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was partially supported by National Basic Research Program of China (973 Program) under contract 2015CB351803; the National Natural Science Foundation of China under contracts 61390514, 61527804, 61572042, and 61520106004; and Sino-German Center (GZ 1025). We also acknowledge the high-performance computing platform of Peking University for providing computational resources.

Author information

Authors and Affiliations

  1. LMAM, School of Mathematical Sciences & IDM, Peking University, Beijing, People’s Republic of China

    Dingquan Li

  2. School of Electrical Engineering and Computer Sciences & IDM, Peking University, Beijing, People’s Republic of China

    Tingting Jiang

Authors
  1. Dingquan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tingting Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tingting Jiang .

Editor information

Editors and Affiliations

  1. School of Mathematical Sciences, Peking University, Beijing, China

    Ming Jiang

  2. Department of Electrical and Computer Engineering, The University of Akron, Akron, OH, USA

    Nathan Ida

  3. Institute of Applied Mathematics, Saarland University, Saarbrücken, Germany

    Alfred K. Louis

  4. Department of Mathematics, Tufts University, Medford, MA, USA

    Eric Todd Quinto

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, D., Jiang, T. (2019). Blur-Specific No-Reference Image Quality Assessment: A Classification and Review of Representative Methods. In: Jiang, M., Ida, N., Louis, A., Quinto, E. (eds) The Proceedings of the International Conference on Sensing and Imaging. ICSI 2017. Lecture Notes in Electrical Engineering, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-91659-0_4

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91658-3

  • Online ISBN: 978-3-319-91659-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation