Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

No-reference image quality assessment based on nonsubsample shearlet transform and natural scene statistics

  • Published:
Optoelectronics Letters Aims and scope Submit manuscript

Abstract

A novel no-reference (NR) image quality assessment (IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain using a non-subsample shearlet transform (NSST), and designs the image quality feature vector to describe images utilizing natural scenes statistical features: coefficient distribution, energy distribution and structural correlation (SC) across orientations and scales. The final image quality is achieved from distortion classification and regression models trained by a support vector machine (SVM). The experimental results on the LIVE2 IQA database indicate that the method can assess image quality effectively, and the extracted features are susceptive to the category and severity of distortion. Furthermore, our proposed method is database independent and has a higher correlation rate and lower root mean squared error (RMSE) with human perception than other high performance NR IQA methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. H. R. Sheikh, A. C. Bovik and L. Cormack, IEEE Transactions on Image Processing 14, 1918 (2005).

    Article  ADS  Google Scholar 

  2. T. Brandao and M. P. Queluz, Signal Processing 88, 822 (2008).

    Article  Google Scholar 

  3. A. Mittal, G. S. Muralidhar and A. C. Bovik, IEEE Signal Processing Letters 19, 75 (2012).

    Article  ADS  Google Scholar 

  4. Q. B. Sang, D. L. Liang, X. J. Wu and C. F. Li, Journal of Optoelectronics·Laser 25, 595 (2014). (in Chinese)

    Google Scholar 

  5. L. Y. Zhou and Z. B. Zhang, Optik 125, 5677 (2014).

    Article  ADS  Google Scholar 

  6. N. D. Narvekar and L. J. Karam, IEEE Transactions on Image Processing 20, 2678 (2011).

    Article  ADS  MathSciNet  Google Scholar 

  7. Y. Li, L. M. Po, X. Xu and L. Feng, Signal Processing Image Communication 29, 748 (2014).

    Article  Google Scholar 

  8. A. K. Moorthy and A. C. Bovik, IEEE Signal Processing Letters 17, 513 (2010).

    Article  ADS  Google Scholar 

  9. A. K. Moorthy and A. C. Bovik, IEEE Transactions on Image Processing 20, 3350 (2011).

    Article  ADS  MathSciNet  Google Scholar 

  10. M. A. Saad, A. C. Bovik and C. Charrier, IEEE Transactions on Image Processing 21, 3339 (2012).

    Article  ADS  MathSciNet  Google Scholar 

  11. A. Mittal, A. K. Moorthy and A. C. Bovik, IEEE Transactions on Image Processing 21, 4695 (2012).

    Article  ADS  MathSciNet  Google Scholar 

  12. Y. Zhang and D. M. Chandler, Journal of Electronic Imaging 22, 043025 (2013).

    Article  ADS  Google Scholar 

  13. H. Y. Yang, X. Y. Wang, P. P. Niu and Y. C. Liu, Neural Networks 57, 152 (2014).

    Article  Google Scholar 

  14. N. E. Lasmar, Y. Stitou and Y. Berthoumieu, Multiscale Skewed Heavy Tailed Model for Texture Analysis, Proceedings of the 16th IEEE International Conference on Image Processing, 2281 (2009).

    Google Scholar 

  15. L. X. Liu, H. P. Dong, H. Huang and A. C. Bovik, Signal Processing Image Communication 29, 494 (2014).

    Article  Google Scholar 

  16. H. R. Sheikh, Z. Wang, L. Cormack and A. C. Bovik, LIVE Image Quality Assessment Database, http://live.ece.utexas.edu/research/quality.

  17. C. C. Chang and C. J. Lin, Acm. Transactions on Intelligent Systems & Technology 2, 389 (2011).

    Article  Google Scholar 

  18. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, IEEE Transactions on Image Processing 13, 600 (2004).

    Article  ADS  Google Scholar 

  19. N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli and F. Battisti, Advances of Modern Radioelectronics 10, 30 (2009).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Photoelectric Measurement and Control, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China

    Guan-jun Wang  (王冠军), Zhi-yong Wu  (吴志勇), Hai-jiao Yun  (云海姣) & Ming Cui  (崔明)

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Guan-jun Wang  (王冠军), Hai-jiao Yun  (云海姣) & Ming Cui  (崔明)

Authors
  1. Guan-jun Wang  (王冠军)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi-yong Wu  (吴志勇)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hai-jiao Yun  (云海姣)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ming Cui  (崔明)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guan-jun Wang  (王冠军).

Additional information

This work has been supported by the National Natural Science Foundation of China (No.61405191), and the Jilin Province Science Foundation for Youths of China (No.20150520102JH).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Gj., Wu, Zy., Yun, Hj. et al. No-reference image quality assessment based on nonsubsample shearlet transform and natural scene statistics. Optoelectron. Lett. 12, 152–156 (2016). https://doi.org/10.1007/s11801-016-5276-2

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11801-016-5276-2

Document code

Search

Navigation