Skip to main content
SpringerLink
Log in

No-reference perceived image quality measurement for multiple distortions

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

No-reference image quality assessment (NR-IQA) metrics play an important role in real-world multimedia applications. Accordingly, many NR-IQA metrics have been proposed in the literature. Most of these proposed metrics predict the visual quality only of images distorted with a single distortion, whereas in the real-world, digital images undergo various steps that contaminate them with multiple distortions before reaching consumers. However, only a few NR-IQA metrics have been proposed for multiple distortions. In this paper, we propose an NR-IQA metric for images distorted with blur and noise, which are added during the image acquisition and transmission process. In the proposed metric, perceived blur and noise scores are estimated separately. These scores are then mapped to the final quality score considering the perceived degree of each type of distortion, the nonlinear functions of the distortions, and the joint effects of the distortions. Weights for each mapping component are learned with subjective data input. Performance comparison of the proposed metric on the blur-noise image dataset of the LIVE multiply distorted (LIVEMD) image quality database confirm that the proposed metric is more effective than the state-of-the-art full-reference and NR IQA metrics.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Chandler DM, Lim KH, Hemami SS (2006) Effects of spatial correlations and global precedence on the visual fidelity of distorted images. In Electronic Imaging International Society for Optics and Photonics 60570F-60570F. doi:10.1117/12.655442

  2. Ferzli R, Karam L (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. doi:10.1109/tip.2008.2011760

    Article  MathSciNet  MATH  Google Scholar 

  3. Gonzalez RC, Woods RE (2008) Digital Image Processing, 3rd edn. Pearson Prentice-Hall, Upper Saddle River

    Google Scholar 

  4. Granrath DJ (1981) The role of human visual models in image processing. Proc IEEE 69(5):552–561. doi:10.1109/proc.1981.12024

    Article  Google Scholar 

  5. Gu K, Zhai G, Yang X, Zhang W (2014) Hybrid no-reference quality metric for singly and multiply distorted images. IEEE Trans Broadcast 60(3):555–567. doi:10.1109/tbc.2014.2344471

    Article  Google Scholar 

  6. Hassen R, Wang Z, Salama M (2013) Image Sharpness Assessment Based on Local Phase Coherence. IEEE Trans Image Process 22(7):2798–2810. doi:10.1109/tip.2013.2251643

    Article  Google Scholar 

  7. Immerkaer J (1996) Fast noise variance estimation. Comput Vis Image Underst 64(2):300–302. doi:10.1006/cviu.1996.0060

    Article  Google Scholar 

  8. Jayaraman D, Mittal A, Moorthy AK, Bovik AC (2012) Objective image quality assessment of multiply distorted images. Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR) IEEE, pp 1693–1697. doi:10.1109/aiccsa.2015.7507114

  9. Larson EC, Chandler DM (2010) Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging 19(1):011006–011006. doi:10.1117/1.3267105

    Article  Google Scholar 

  10. Li Q, Lin W, Fang Y (2016) No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain. IEEE Signal Processing Letters 23(4):541–545. doi:10.1109/lsp.2016.2537321

    Article  Google Scholar 

  11. Liu A, Lin W, Narwaria M (2012) Image quality assessment based on gradient similarity. IEEE Trans Image Process 21(4):1500–1512. doi:10.1109/tip.2011.2175935

    Article  MathSciNet  MATH  Google Scholar 

  12. Liu X, Tanaka M, Okutomi M (2013) Single-image noise level estimation for blind denoising. IEEE Trans Image Process 22(12):5226–5237. doi:10.1109/tip.2013.2283400

    Article  Google Scholar 

  13. Lu Y, Xie F, Liu T, Jiang Z, Tao D (2015) No reference quality assessment for multiply-distorted images based on an improved bag-of-words model. IEEE Signal Processing Letters 22(10):1811–1815. doi:10.1109/lsp.2015.2436908

    Article  Google Scholar 

  14. Ma L, Li S, Zhang F, Ngan KN (2011) Reduced-reference image quality assessment using reorganized DCT-based image representation. IEEE Transactions on Multimedia 13(4):824–829. doi:10.1109/tmm.2011.2109701

    Article  Google Scholar 

  15. Marziliano P, Dufaux F, Winkler S, Ebrahimi T (2004) Perceptual blur and ringing metrics: Applications to Jpeg 2000. Signal Process Image Commun 19(2):163–172. doi:10.1016/j.image.2003.08.003

    Article  Google Scholar 

  16. Mittal A, Moorthy AK, Bovik AC (2012) No reference Image Quality Assessment in the Spatial Domain. IEEE Trans Image Process 21(12):4695–4708. doi:10.1109/tip.2012.2214050

    Article  MathSciNet  MATH  Google Scholar 

  17. Mittal A, Soundararajan R, Bovik AC (2013) Making a Completely Blind Image Quality Analyzer. IEEE Signal Processing Letters 20(3):209–212. doi:10.1109/lsp.2012.2227726

    Article  Google Scholar 

  18. Moorthy AK, Bovik AC (2010) A Two step Framework for Constructing Blind Image Quality Indices. IEEE Signal Processing Letters 17(5):513–516. doi:10.1109/lsp.2010.2043888

    Article  Google Scholar 

  19. Narvekar N, Karam L (2011) A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD). IEEE Trans Image Process 20(9):2678–2683. doi:10.1109/tip.2011.2131660

    Article  MathSciNet  MATH  Google Scholar 

  20. Ponomarenko N, Jin L, Leremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F, CCJ K (2015) Image database TID2013: Peculiarities, results and perspectives. Signal Process Image Commun 30:57–77. doi:10.1016/j.image.2014.10.009

    Article  Google Scholar 

  21. Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444. doi:10.1109/tip.2005.859378

    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. doi:10.1109/tip.2006.881959

    Article  Google Scholar 

  23. Sheikh HR, Wang Z, Cormack L, Bovik AC (2005) LIVE image quality assessment database release 2. Available: http://live.ece.utexas.edu/research/quality [online]

  24. Sodhani P, Bordia A, Karthik K (2015) Blind Content Independent Noise Estimation for Multimedia Applications. Procedia Computer Science 54:549–557. doi:10.1016/j.procs.2015.06.063

    Article  Google Scholar 

  25. 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. doi:10.1109/TIP.2003.819861

    Article  Google Scholar 

  26. Wang Z, Li Q (2011) Information content weighting for perceptual image quality assessment. IEEE Trans Image Process 20(5):1185–1198. doi:10.1109/tip.2010.2092435

    Article  MathSciNet  MATH  Google Scholar 

  27. Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers 2:1398–1402. doi:10.1109/acssc.2003.1292216

    Article  Google Scholar 

  28. Xue W, Zhang L, Mou X (2013) Learning without human scores for blind image quality assessment. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 995–1002. doi:10.1109/cvpr.2013.133

  29. Xue W, Zhang L, Mou X, Bovik AC (2014) Gradient magnitude similarity deviation: A highly efficient perceptual image quality index. IEEE Trans Image Process 23(2):684–695. doi:10.1109/tip.2013.2293423

    Article  MathSciNet  MATH  Google Scholar 

  30. Zhang L, Li H (2012) SR-SIM: A fast and high performance IQA index based on spectral residual. 19th IEEE International Conference on Image Processing, pp 1473–1476. doi:10.1109/icip.2012.6467149

  31. Zhang L, Shen Y, Li H (2014) VSI: A visual saliency-induced index for perceptual image quality assessment. IEEE Trans Image Process 23(10):4270–4281. doi:10.1109/tip.2014.2346028

    Article  MathSciNet  MATH  Google Scholar 

  32. Zhang L, Zhang L, & Mou X. (2010, September) RFSIM: a feature based image quality assessment metric using Riesz transforms. 17th IEEE International Conference on Image Processing, pp 321–324. doi:10.1109/icip.2010.5649275

  33. Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: A feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386. doi:10.1109/tip.2011.2109730

    Article  MathSciNet  MATH  Google Scholar 

  34. Zhu X, Milanfar P (2010) Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans Image Process 19(12):3116–3132. doi:10.1109/tip.2010.2052820

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Engineering, Pune, Department of E&TC, Savitribai Phule Pune University, Wellesely Road, Shivaji Nagar, Pune, Maharashtra, 411005, India

    Anupama B. Lamb

  2. Cummins College of Engineering for Women, Department of E&TC, Savitribai Phule Pune University, Karvenagar, Pune, Maharashtra, 411052, India

    Madhuri Khambete

Authors
  1. Anupama B. Lamb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Madhuri Khambete
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anupama B. Lamb.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lamb, A.B., Khambete, M. No-reference perceived image quality measurement for multiple distortions. Multimed Tools Appl 77, 8653–8675 (2018). https://doi.org/10.1007/s11042-017-4761-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4761-3

Keywords

Search

Navigation