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

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Artificial Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6113))

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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.

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References

  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. 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. Eskicioglu, A., Fisher, P., Chen, S.: Image Quality Measures and Their Performance. IEEE Trans. Comm. 43(12), 2959–2965 (1995)

    Article  Google Scholar 

  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. 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:10.1117/12.644226

    Google Scholar 

  6. Li, X.: Blind Image Quality Assessment. In: Proc. IEEE Int. Conf. Image Proc., pp. 449–452 (2002)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  10. Meesters, L., Martens, J.-B.: A Single-Ended Blockiness Measure for JPEG-Coded Images. Signal Processing 82(3), 369–387 (2002)

    Article  MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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. 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)

    Article  Google Scholar 

  21. Sheikh, H.R., Bovik, A.C.: Image Information and Visual Quality. IEEE Trans. Image Processing 15(2), 430–444 (2006)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  24. Wang, Z., Bovik, A.: A Universal Image Quality Index. IEEE Signal Processing Letters 9(3), 81–84 (2002)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. VQEG, Final Report on the Validation of Objective Models of Video Quality Assessment (August 2003), http://www.vqeg.org

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Chair of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology, Szczecin, 26. Kwietnia 10, 71-126, Szczecin, Poland

    Krzysztof Okarma

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  1. Krzysztof Okarma
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź,, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics,, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA,  

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

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Okarma, K. (2010). 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) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_67

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  • DOI: https://doi.org/10.1007/978-3-642-13208-7_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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