Skip to main content
SpringerLink
Log in

An Image Quality Metric with Reference for Multiply Distorted Image

  • Conference paper
  • First Online:
Advanced Concepts for Intelligent Vision Systems (ACIVS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10016))

  • 2241 Accesses

Abstract

In this paper, we propose a global framework to estimate the quality of multiply degraded images with reference (Full Reference approach). Our method is based on features fusion using a Support Vector Regression (SVR) model. The selected features are here some quality indexes obtained by comparing the reference image and its degraded version. Some of these features are based on Human Visual System (HVS), while some others are based on structural information or mutual information. The proposed method has been evaluated through the LIVE Multiply Distorted Image Quality Database, composed of 450 degraded images. The obtained results are compared to 12 recent image quality metrics.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Pedersen, M., Hardeberg, J.Y.: Survey of full-reference image quality metrics. Høgskolen i Gjøviks rapportserie, Gjøvik, Norway, Number 5 (2009). ISSN: 1890-520X

    Google Scholar 

  2. Sheikh, H.R., Wang, Z., Cormack, Z., Bovik, A.C.: Live image quality assessment database (2006). http://live.ece.utexas.eduesearch/quality

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

    Article  Google Scholar 

  4. Le Callet, P., Autrusseau, F.: Subjective quality assessment IRCCyN/IVC database (2005), http://www.irccyn.ec-nantes.fr/ivcdb/

  5. Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19, 1–21 (2010)

    Google Scholar 

  6. Tourancheau, S., Autrusseau, F., Sazzad, P., Horita,Y.: Impact of the subjective dataset on the performance of image quality metrics. In: IEEE International Conference on Image Processing (2008)

    Google Scholar 

  7. http://foulard.ece.cornell.edu/dmc27/vsnr/vsnr.html

  8. Jayaraman, D., Mittal, A., Moorthy, A.K., Bovik, A.C.: Objective quality assessment of multiply distorted images. In: Asilomar Conference on Signals, Systems and Computers, pp. 1693–1697 (2012)

    Google Scholar 

  9. Dosselman, R., Yang, X.D.: A comprehensive assessment of the structural similarity index. Signal Image Video Process. 5, 81–91 (2011)

    Article  Google Scholar 

  10. Okarna, K.: Quality assessment of images with multiple distortions using combined metrics. Elektronika Ir Elektrotechnika 20, 128–131 (2014)

    Google Scholar 

  11. Wang, Z., Bovik, A.C., Lu, L.: Why is image quality assessment so difficult? In: IEEE International Conference on Acoustics, Speech & Signal Processing (2002)

    Google Scholar 

  12. Cornsweet, T.N.: Visual Perception. Academic Press, New York (1970)

    Google Scholar 

  13. Egiazarian, K., Astola, J. Ponomarenko, N., Lukin, V., Battisti, F., Carli, M.: New full-reference quality metrics based on HVS. In: International Workshop on Video Processing and Quality Metrics (2006)

    Google Scholar 

  14. Ponomarenko, N., Silvestri, F., Egiazarian, K., Carli, M., Astola, J., Lukin, V.: On between-coefficient contrast masking of DCT basis functions. In: International Workshop on Video Processing and Quality Metrics (2007)

    Google Scholar 

  15. Chandler, D.M., Hemami, S.S.: VSNR: a wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans. Image Process. 16, 2284–2298 (2007)

    Article  MathSciNet  Google Scholar 

  16. Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  17. Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1398–1402 (2003)

    Google Scholar 

  18. Chen, G.H., Yang, C.L., Po, L.M., Xie, S.L.: Edge-based structural similarity for image quality assessment. In: IEEE International Conference in Acoustics, Speech and Signal Processing, vol. 2, pp. 933–936 (2006)

    Google Scholar 

  19. Chen, G., Yang, C., Xie, S.: Gradient-based structural similarity for image quality assessment. In: IEEE International Conference on Image Processing, pp. 2929–2932 (2006)

    Google Scholar 

  20. Wang, Z., Simoncelli, E.: Translation insensitive image similarity in complex wavelet domain. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 573–576 (2005)

    Google Scholar 

  21. Daly, S.: The visible differences predictor: an algorithm for the assessment of image fidelity. In: Digital Images and Human Vision, chap. 14, pp. 179–206. MIT Press (1993)

    Google Scholar 

  22. Watson, A.: The cortex transform: rapid computation of simulated neural images. Comput. Vis. Graph. Image Process. 39, 311–327 (1987)

    Article  Google Scholar 

  23. Mantiuk, R., Daly, S., Myszkowski, K., Seidel, H.P.: Predicting visible differences in high dynamic range images - model and its calibration. In: Human Vision and Electronic Imaging X, IS&T/SPIE’s, pp. 204–214 (2005)

    Google Scholar 

  24. Carnec, M., Le Callet, P., Barba, D.: Objective quality assessment of color images based on a generic perceptual reduced reference. Signal Process. Image Commun. 23(4), 239–256 (2008)

    Article  Google Scholar 

  25. Chetouani, A., Beghdadi, A., Deriche, M.: A hybrid system for distortion classification and image quality evaluation. J. Signal Process. Image Commun. 9, 948–960 (2012)

    Article  Google Scholar 

  26. Sheikh, H.R., Bovik, A.C., de Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14, 2117–2128 (2005)

    Article  Google Scholar 

  27. Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15, 430–444 (2006)

    Article  Google Scholar 

  28. Reenu, M., David, D., Raj, S.S.A., Nair, M.S.: Wavelet based sharp features (WASH): an image quality assessment metric based on HVS. In: International Conference on Advanced Computing, Networking and Security, pp. 79–83 (2013)

    Google Scholar 

  29. Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9, 81–84 (2002)

    Article  Google Scholar 

  30. http://asi.insa-rouen.fr/enseignants/~arakoto/toolbox/

  31. Damera-Venkata, N., Kite, T., Geisler, W., Evans, B., Bovik, A.C.: Image quality assessment based on a degradation model. IEEE Trans. Image Process. 9, 636–650 (2000)

    Article  Google Scholar 

  32. Chetouani, A.: A reduced reference image quality assessment for multiply distorted images. In: International Conference on Computer Systems and Applications (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Orleans, 12 rue de Blois, 45067, Orléans, France

    Aladine Chetouani

Authors
  1. Aladine Chetouani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aladine Chetouani .

Editor information

Editors and Affiliations

  1. Université Paris-Sud 11 , Orsay, France

    Jacques Blanc-Talon

  2. University of Salento , Lecce, Lecce, Italy

    Cosimo Distante

  3. Ghent University , Gent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre , Sydney, New South Wales, Australia

    Dan Popescu

  5. University of Antwerp , Wilrijk, Belgium

    Paul Scheunders

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Chetouani, A. (2016). An Image Quality Metric with Reference for Multiply Distorted Image. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48680-2_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48679-6

  • Online ISBN: 978-3-319-48680-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation