Advertisement

Combined Full-Reference Image Quality Metric Linearly Correlated with Subjective Assessment

  • Krzysztof Okarma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6113)

Abstract

In the paper a new combined image quality metric is proposed, which is based on three methods previously described by various researchers. The main advantage of the presented approach is the strong linear correlation with the subjective scores without additional nonlinear mapping. The values and the obtained correlation coefficients of the proposed metric have been compared with some other state-of-art ones using two largest publicly available image databases including the subjective quality scores.

Keywords

Image quality assessment 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bovik, A., Liu, S.: DCT-domain Blind Measurement of Blocking Artifacts in DCT-Coded Images. In: Proc. Int. Conf. Acoustics, Speech and Signal Processing, Salt Lake City, USA, pp. 1725–1728 (2001)Google Scholar
  2. 2.
    Carnec, M., Le Callet, P., Barba, P.: An Image Quality Assessment Method Based on Perception of Structural Information. In: Proc. Int. Conf. Image Processing, Barcelona, Spain, vol. 2, pp. 185–188 (2003)Google Scholar
  3. 3.
    Eskicioglu, A., Fisher, P., Chen, S.: Image Quality Measures and Their Performance. IEEE Trans. Comm. 43(12), 2959–2965 (1995)CrossRefGoogle Scholar
  4. 4.
    Eskicioglu, A.: Quality Measurement for Monochrome Compressed Images in the Past 25 Years. In: Proc. IEEE Int. Conf. Acoust. Speech Signal Process, Istanbul, Turkey, pp. 1907–1910 (2000)Google Scholar
  5. 5.
    Girshtel, E., Slobodyan, V., Weissman, J., Eskicioglu, A.: Comparison of Three Full–Reference Color Image Quality Measures. In: Proc. SPIE of 18th IS&T/SPIE Annual Symposium on Electronic Imaging, Image Quality and System Performance, San Jose, CA, vol. 6059 (2006), doi:10.1117/12.644226Google Scholar
  6. 6.
    Li, X.: Blind Image Quality Assessment. In: Proc. IEEE Int. Conf. Image Proc., pp. 449–452 (2002)Google Scholar
  7. 7.
    Mahmoudi-Aznaveh, A., Mansouri, A., Torkamani-Azar, F., Eslami, M.: Image Quality Measurement Besides Distortion Type Classifying. Optical Review 16(1), 30–34 (2009)CrossRefGoogle Scholar
  8. 8.
    Marziliano, P., Dufaux, F., Winkler, S., Ebrahimi, T.: A No-Reference Perceptual Blur Metric. In: Proc. IEEE Int. Conf. Image Processing, Rochester, USA, pp. 57–60 (2002)Google Scholar
  9. 9.
    Mansouri, A., Mahmoudi-Aznaveh, A., Torkamani-Azar, F., Jahanshahi, J.A.: Image Quality Assessment Using the Singular Value Decomposition Theorem. Optical Review 16(2), 49–53 (2009)CrossRefGoogle Scholar
  10. 10.
    Meesters, L., Martens, J.-B.: A Single-Ended Blockiness Measure for JPEG-Coded Images. Signal Processing 82(3), 369–387 (2002)zbMATHCrossRefGoogle Scholar
  11. 11.
    Okarma, K., Lech, P.: Monte Carlo Based Algorithm for Fast Preliminary Video Analysis. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part I. LNCS, vol. 5101, pp. 790–799. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Okarma, K., Lech, P.: A Statistical Reduced-Reference Approach to Digital Image Quality Assessment. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds.) ICCVG 2008. LNCS, vol. 5337, pp. 43–54. Springer, Heidelberg (2009)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    Okarma, K.: Two-Dimensional Windowing in the Structural Similarity Index for the Colour Image Quality Assessment. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 501–508. Springer, Heidelberg (2009)Google Scholar
  15. 15.
    Ong, E.-P., Lin, L.W., Yang, Z., Yao, S., Pan, F., Jiang, L., Moschetti, F.: A No-Reference Quality Metric for Measuring Image Blur. In: Proc. 7th Int. Symp. Signal Processing and Its Applications, Paris, France, pp. 469–472 (2003)Google Scholar
  16. 16.
    Ponomarenko, N., Carli, M., Lukin, V., Egiazarian, K., Astola, J., Battisti, F.: Color Image Database for Evaluation of Image Quality Metrics. In: Proc. Int. Workshop on Multimedia Signal Processing, Cairns, Queensland, Australia, pp. 403–408 (2008)Google Scholar
  17. 17.
    Ponomarenko, N., Battisti, F., Egiazarian, K., Astola, J., Lukin, V.: Metrics Performance Comparison for Color Image Database. In: Proc. 4th Int. Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, Arizona, USA (2009)Google Scholar
  18. 18.
    Sendashonga, M., Labeau, F.: Low Complexity Image Quality Assessment Using Frequency Domain Transforms. In: Proc. IEEE Int. Conf. Image Processing, pp. 385–388 (2006)Google Scholar
  19. 19.
    Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE Image Quality Assessment Database Release 2, http://live.ece.utexas.edu/research/quality
  20. 20.
    Sheikh, H.R., Bovik, A.C., de Veciana, G.: An Information Fidelity Criterion for Image Quality Assessment Using Natural Scene Statistics. IEEE Trans. Image Processing 14(12), 2117–2128 (2005)CrossRefGoogle Scholar
  21. 21.
    Sheikh, H.R., Bovik, A.C.: Image Information and Visual Quality. IEEE Trans. Image Processing 15(2), 430–444 (2006)CrossRefGoogle Scholar
  22. 22.
    Shnayderman, A., Gusev, A., Eskicioglu, A.: A Multidimensional Image Quality Measure Using Singular Value Decomposition. In: Proc. SPIE Image Quality and Syst. Perf., vol. 5294(1), pp. 82–92 (2003)Google Scholar
  23. 23.
    Shnayderman, A., Gusev, A., Eskicioglu, A.: An SVD-Based Gray-Scale Image Quality Measure for Local and Global Assessment. IEEE Trans. Image Processing 15(2), 422–429 (2006)CrossRefGoogle Scholar
  24. 24.
    Wang, Z., Bovik, A.: A Universal Image Quality Index. IEEE Signal Processing Letters 9(3), 81–84 (2002)CrossRefGoogle Scholar
  25. 25.
    Wang, Z., Bovik, A., Evans, B.: Blind Measurement of Blocking Artifacts in Images. In: Proc. IEEE Int. Conf. Image Processing, pp. 981–984 (2000)Google Scholar
  26. 26.
    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
  27. 27.
    Wang, Z., Simoncelli, E., Bovik, A.: Multi-Scale Structural Similarity for Image Quality Assessment. In: Proc. 37th IEEE Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA (2003)Google Scholar
  28. 28.
    Wang, Z., Sheikh, H., Bovik, A.: No-Reference Perceptual Quality Assessment of JPEG Compressed Images. In: Proc. IEEE Int. Conf. Image Processing, Rochester, USA, pp. 477–480 (2002)Google Scholar
  29. 29.
    Wang, Z., Simoncelli, E.: Reduced-Reference Image Quality Assessment using a Wavelet-Domain Natural Image Statistic Model. In: Proceedings of SPIE, Proc. Human Vision and Electronic Imaging Conference, San Jose, USA, vol. 5666, pp. 149–159 (2005)Google Scholar
  30. 30.
    VQEG, Final Report on the Validation of Objective Models of Video Quality Assessment (August 2003), http://www.vqeg.org

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

Personalised recommendations