Blind Image Quality Assessment Through Wakeby Statistics Model

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)

Abstract

In this paper, a new universal blind image quality assessment algorithm is proposed that works in presence of various distortions. The proposed algorithm uses natural scene statistics in spatial domain for generating Wakeby distribution statistical model to extract quality aware features. The features are fed to an SVM (support vector machine) regression model to predict quality score of input image without any information about the distortions type or reference image. Experimental results show that the image quality score obtained by the proposed method has higher correlation with respect to human perceptual opinions and it’s superior in some distortions comparing to some full-reference and other blind image quality methods.

Keywords

Blind image quality assessment Natural scene statistics of special domain Wakeby distribution model Support vector machine 

References

  1. 1.
    Narvekar, N.D., Karam, L.J.: A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Trans. Image Process. 20, 2678–2683 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Zaric, A., Loncaric, M., Tralic, D., Brzica, M., Dumic, E., Grgic, S.: Image quality assessment - comparison of objective measures with results of subjective test. In: ELMAR, 2010 proceedings, pp. 113–118 (2010)Google Scholar
  3. 3.
    Zhou, W., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)CrossRefGoogle Scholar
  4. 4.
    Dacheng, T., Xuelong, L., Wen, L., Xinbo, G.: Reduced-reference IQA in contourlet domain. IEEE Trans. Syst. Man Cybern. Part B Cybern. 39, 1623–1627 (2009)CrossRefGoogle Scholar
  5. 5.
    Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21, 4695–4708 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Li, L., Lin, W., Wang, X., Yang, G., Bahrami, K., Kot, A.C.: No–reference image blur assessment based on discrete orthogonal moments. IEEE Trans. Cybern. PP, 1–1 (2015)Google Scholar
  7. 7.
    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, 2089–2098 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Saad, M.A., Bovik, A.C., Charrier, C.: Model-based blind image quality assessment using natural DCT statistics. IEEE Trans. Image Process. 21, 3339–3352 (2011)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Ponomarenko, N., Ieremeiev, O., Lukin, V., Egiazarian, K., Jin, L., Astola, J., et al., Color image database TID2013: peculiarities and preliminary results. In: 2013 4th European Workshop on Visual Information Processing (EUVIP), pp. 106–111 (2013)Google Scholar
  10. 10.
    Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image Process. 21, 3339–3352 (2012)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Liu, L., Liu, B., Huang, H., Bovik, A.C.: No-reference image quality assessment based on spatial and spectral entropies. Sig. Process. Image Commun. 29, 856–863 (2014)CrossRefGoogle Scholar
  12. 12.
    Moorthy, A.K., Bovik, A.C.: Statistics of natural image distortions. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 962–965 (2010)Google Scholar
  13. 13.
    Shuhong, J., Abdalmajeed, S., Wei, L., Ruxuan, W.: Totally blind image quality assessment algorithm based on weibull statistics of natural scenes. Inf. Technol. J. 13, 1548–1554 (2014)CrossRefGoogle Scholar
  14. 14.
    Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: 2004. Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, pp. 1398–1402 (2003)Google Scholar
  15. 15.
    Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12, 1207–1245 (2000)CrossRefGoogle Scholar
  16. 16.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)CrossRefGoogle Scholar
  17. 17.
    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, 3440–3451 (2006)CrossRefGoogle Scholar
  18. 18.
    Rohaly, A.M., Libert, J., Corriveau, P., Webster, A.: Final report from the video quality experts group on the validation of objective models of video quality assessment, ITU-T Standards Contribution COM, pp. 9–80 (2000)Google Scholar
  19. 19.
    Huynh-Thu, Q., Ghanbari, M.: Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44, 800–801 (2008)CrossRefGoogle Scholar
  20. 20.
    Griffiths, G.A.: A theoretically based Wakeby distribution for annual flood series. Hydrol. Sci. J. 34, 231–248 (1989)CrossRefGoogle Scholar
  21. 21.
    Öztekin, T.: Estimation of the parameters of wakeby distribution by a numerical least squares method and applying it to the annual peak flows of Turkish rivers. Water Resour. Manage. 25, 1299–1313 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Computer Science and EngineeringShahid Beheshti University G.CTehranIran

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