Hybrid Feature Similarity Approach to Full-Reference Image Quality Assessment

  • Krzysztof Okarma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)


In the paper the Hybrid Feature Similarity metric is proposed based on the combination of two recently proposed objective image quality assessment methods - Riesz transform based Feature Similarity metric and Feature Similarity index. Both of them have good performance in comparison to most “state-of-the-art” quality metrics but highly linear correlation with subjective scores requires an additional nonlinear mapping for tuning to each dataset. In order to overcome this problem and obtain high quality prediction accuracy the nonlinear combination of both metrics is proposed leading to better performance than using each of the metrics separately. The experiments conducted in order to propose the weighting coefficients for both metrics have been performed using TID2008 dataset which is currently the largest and most comprehensive publicly available image quality assessment database, containing 1700 images together with their subjective quality evaluations. The verification of the obtained results has been also conducted using some other relevant benchmark databases.


image quality assessment feature similarity image analysis 


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  1. 1.
    Zhang, L., Zhang, L., Mou, X.: RFSIM: A feature based image quality assessment metric using Riesz transforms. In: Proc. 17th IEEE Int. Conf. Image Processing, Hong Kong, China, pp. 321–324 (2010)Google Scholar
  2. 2.
    Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: A Feature Similarity index for image quality assessment. IEEE Trans. Image Processing 20(8), 2378–2386 (2011)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Okarma, K.: Colour Image Quality Assessment Using Structural Similarity Index and Singular Value Decomposition. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds.) ICCVG 2008. LNCS, vol. 5337, pp. 55–65. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Okarma, K.: Colour Image Quality Assessment Using the Combined Full-reference Metric. In: Burduk, R., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds.) Computer Recognition Systems 4. AISC, vol. 95, pp. 287–296. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Wang, Z., Bovik, A.: A universal image quality index. IEEE Signal Processing Letters 9(3), 81–84 (2002)CrossRefGoogle Scholar
  6. 6.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error measurement to Structural Similarity. IEEE Trans. Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  7. 7.
    Wang, Z., Simoncelli, E., Bovik, A.: Multi-Scale Structural Similarity for image quality assessment. In: Proc. 37th IEEE Asilomar Conf. Signals, Systems and Computers, Pacific Grove, California (2003)Google Scholar
  8. 8.
    Sampat, M., Wang, Z., Gupta, S., Bovik, A., Markey, M.: Complex wavelet structural similarity: A new image similarity index. IEEE Trans. Image Processing 18(11), 2385–2401 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Liu, Z., Laganière, R.: Phase congruence measurement for image similarity assessment. Pattern Recognition Letters 28(1), 166–172 (2007)CrossRefGoogle Scholar
  10. 10.
    Parvez Sazzad, Z., Kawayoke, Y., Horita, Y.: MICT/Toyama image quality evaluation database (2000),
  11. 11.
    Sheikh, H., Wang, Z., Cormack, L., Bovik, A.: LIVE Image Quality Assessment Database Release 2 (2005),
  12. 12.
    Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID 2008 - a database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics 10, 30–45 (2009)Google Scholar
  13. 13.
    Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging 19(1), 011006 (2010)Google Scholar
  14. 14.
    Engelke, U., Zepernick, H.-J., Kusuma, T.: Subjective quality assessment for wireless image communication: The Wireless Imaging Quality database. In: Proc. 5th Int. Workshop Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, Arizona (2010)Google Scholar
  15. 15.
    Le Callet, P., Autrusseau, F.: Subjective quality assessment IRCCyN/IVC database (2005),
  16. 16.
    Chandler, D., Hemami, S.: VSNR: A wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans. Image Processing 16(9), 2284–2298 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Tourancheau, S., Autrusseau, F., Sazzad, Z., Horita, Y.: Impact of subjective dataset on the performance of image quality metrics. In: Proc. 15th IEEE Int. Conf. Image Processing, San Diego, California, pp. 365–368 (2008)Google Scholar
  18. 18.
    Okarma, K.: Combined Full-Reference Image Quality Metric Linearly Correlated with Subjective Assessment. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part I. LNCS, vol. 6113, pp. 539–546. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Liu, A., Lin, W., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing 21(4), 1500–1512 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Krzysztof Okarma
    • 1
  1. 1.Faculty of Electrical Engineering, Department of Signal Processing and Multimedia EngineeringWest Pomeranian University of Technology, SzczecinSzczecinPoland

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