Image Quality Assessment with Structural Similarity Using Wavelet Families at Various Decompositions

  • Jayesh Deorao Ruikar
  • A. K. Sinha
  • Saurabh Chaudhury
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)


Wavelet transform is one of the most active areas of research in the image processing. This paper gives analysis of a very well known objective image quality metric, so called Structural similarity, MSE and PSNR which measures visual quality between two images. This paper presents the joint scheme of wavelet transform with structural similarity for evaluating the quality of image automatically. In the first part of algorithm, each distorted as well as original image are decomposed into three levels and in second part, these coefficient are used to calculate the structural similarity index, MSE and PSNR. The predictive performance of image quality based on the wavelet families like db5, haar (db1), coif1 with one, two and three level of decomposition is figured out. The algorithm performance includes the correlation measurement like Pearson, Kendall, and Spearman correlation between the objective evaluations with subjective one.


Wavelet image quality Objective image quality measurement Subjective assessment 


  1. 1.
    Wang, Z., Bovik, A.C.: Modern image quality assessment. In: Synthesis Lectures on Image, Video, and Multimedia Processing, vol. 2 (1) (2006)Google Scholar
  2. 2.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  3. 3.
    Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. Invited Paper, IEEE Asilomar Conference on Signals, Systems and Computers (2003)Google Scholar
  4. 4.
    Yong, D., Shaoze, W., Dong, Z.: Full-reference image quality assessment using statistical local correlation. Electron. Lett. 50(2), 79–81 (2014)Google Scholar
  5. 5.
    Demirtas, A.M., Reibman, A.R., Jafarkhani, H.: Full-reference quality estimation for images with different spatial resolutions. IEEE Trans. Image Process. 23(5), 2069–2080 (2014)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006)CrossRefGoogle Scholar
  7. 7.
    Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3440–3451 (2006)CrossRefGoogle Scholar
  8. 8.
    Zaramensky, D.A., Priorov, A.L., Bekrenev, V.A., Soloviev, V.E.: No-reference quality assessment of wavelet-compressed images. In: IEEE EUROCON, pp. 1332–1337 (2009)Google Scholar
  9. 9.
    Ji, S., Qin, L., Erlebacher, G.: Hybrid no-reference natural image quality assessment of noisy, blurry, JPEG2000, and JPEG Images. IEEE Trans. Image Process. 20(8), 2089–2098 (2011)Google Scholar
  10. 10.
    Ke, G., Guangtao, Z., Xiaokang, Y., Wenjun, Z., Longfei, L.: No-reference image quality assessment metric by combining free energy theory and structural degradation model. IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2013)Google Scholar
  11. 11.
    Golestaneh, S.A., Chandler, D.M.: No-reference quality assessment of jpeg images via a quality relevance map. IEEE Signal Process. Lett. 21(2), 155–158 (2014)CrossRefGoogle Scholar
  12. 12.
    Qiang, L., Zhou, W.: Reduced-reference image quality assessment using divisive normalization-based image representation. IEEE J. Selected Topics Signal Process. 3(2), 202–211 (2009)Google Scholar
  13. 13.
    Rehman, A., Zhou, W.: Reduced-reference image quality assessment by structural similarity estimation. IEEE Trans. Image Process. 21(8), 3378–3389 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Jinjian, W., Weisi, L., Guangming, S., Anmin, L.: Reduced-reference image quality assessment with visual information fidelity. IEEE Trans. Multimedia 15(7), 1700–1705 (2013)Google Scholar
  15. 15.
    Soundararajan, R., Bovik, A.C.: RRED indices: reduced reference entropic differencing for image quality assessment. IEEE Trans. Image Process. 21(2), 517–526 (2012)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Chun-Ling, Y., Wen-Rui, G., Lai-Man, P.: Discrete wavelet transform-based structural similarity for image quality assessment. In: 15th IEEE International Conference on Image Processing, pp. 377–380 (2008)Google Scholar
  17. 17.
    Guo-Li, J., Xiao-Ming, N., Hae-Young, B.: A full-reference image quality assessment algorithm based on haar wavelet transform. International Conference on Computer Science and Software Engineering, pp. 791–794 (2008)Google Scholar
  18. 18.
    Rezazadeh, S., Coulombe, S.: A novel discrete wavelet transform framework for full reference image quality assessment. SIViP 7(3), 559–573 (2013)CrossRefGoogle Scholar
  19. 19.
    Wang, Z., Ligang, L., Bovik, A.C.: Video quality assessment based on structural distortion measurement. Sig. Process. Image Commun. 19(2), 121–132 (2004)CrossRefGoogle Scholar
  20. 20.
    Zhou, W., Bovik, A.C.: A universal image quality index. Signal Processing Lett. IEEE, 9(3), 81–84 (2002)Google Scholar
  21. 21.
    Zhou, W., Bovik, A.C.: Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE Signal Proc. Magaz. 26(1), 98–117 (2009)Google Scholar
  22. 22.
    Ruikar, J.D., Sinha, A.K., Chaudhury, S.: Review of image enhancement techniques. In: International Conference on Information Technology in Signal and Image Processing—ITSIP 2013 (2013)Google Scholar
  23. 23.
    Ruikar, J.D., Sinha, A.K., Chaudhury, S.: Structural similarity and correlation based filtering for image quality assessment. In: IEEE International Conference on Communication and Signal Processing—ICCSP’ 14 (2014)Google Scholar
  24. 24.
    Ponomarenko, N.: Tampere Image Database 2008 version 1.0, (2008).
  25. 25.
    Chandler, D.M.: CSIQ Image Database (2010).
  26. 26.
    Sheikh, H.R.: LIVE Image Quality Assessment Database, Release 2, (2005).
  27. 27.
    Le Callet, P.: Subjective quality assessment IRCCyN/IVC Database, 2005.

Copyright information

© Springer India 2016

Authors and Affiliations

  • Jayesh Deorao Ruikar
    • 1
  • A. K. Sinha
    • 1
  • Saurabh Chaudhury
    • 1
  1. 1.National Institute of TechnologySilcharIndia

Personalised recommendations