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Reduced reference image quality assessment based on statistics in empirical mode decomposition domain

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Abstract

This paper deals with the image quality assessment (IQA) task using a natural image statistics approach. A reduced reference (RRIQA) measure based on the bidimensional empirical mode decomposition is introduced. First, we decompose both, reference and distorted images, into intrinsic mode functions (IMF) and then we use the generalized Gaussian density (GGD) to model IMF coefficients of the reference image. Finally, we measure the impairment of a distorted image by fitting error between the IMF coefficients histogram of the distorted image and the estimated IMF coefficients distribution of the reference image, using the Kullback–Leibler divergence (KLD). Furthermore, to predict the quality, we propose a new support vector machine-based (SVM) classification approach as an alternative to logistic function-based regression. In order to validate the proposed measure, three benchmark datasets are involved in our experiments. Results demonstrate that the proposed metric compare favorably with alternative solutions for a wide range of degradation encountered in practical situations.

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References

  1. Wang, Z., Bovik, A., Sheikh, C.H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 1624–1639 (2004)

    Article  Google Scholar 

  2. Wang, Z., Sheikh, H.R., Bovik, A.C.: No-reference perceptual quality assessment of JPEG compressed images. IEEE International Conference on Image Processing, vol. 1, pp. 477–480. Rochester, New York, USA (2002)

  3. Campaner, J.-N., Cherifi, H.: Picture quality evaluation strategy using a watermarking technique. 4th EURASIP Conference Focused on Video/Image Processing and Multimedia Communications, vol. 2, pp. 721–726 (2003)

  4. Saviotti, S., Mapelli, F., Lancini, R.: Video quality analysis using a watermarking technique. In: Proceedingd of WIAMIS2004, Lisbon, Portugal (April 2004)

  5. Kusuma T.M., Zepernick, H.-J.: A reduced-reference perceptual quality metric for in-service image quality assessment. Joint First Workshop on Mobile Future and Symposium on Trends in Communications, pp. 71–74. Western Australian Telecommunications Research Institute, Nedlands, WA, Australia (2003)

  6. Carnec, M., Le Callet, P., Barba, D.: Objective quality assessment of color images based on a generic perceptual reduced reference. Signal Process. Image Commun. 23(4), 239–256 (2008)

    Article  Google Scholar 

  7. Wang, Z., Simoncelli, E.: Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. SPIE Human Vision and Electronic Imaging, vol. 5666, pp. 149–159. San Jose CA, USA (2005)

  8. Li, Q., Wang, Z.: Reduced-reference image quality assessment using divisive normalization-based image representation. IEEE J. Sel. Top. Signal Process. 3(2), 202–211 (2009)

    Article  Google Scholar 

  9. Wufeng, X., Xuanqin, M.: Reduced reference image quality assessment based on Weibull statistics. The International Workshop on Quality of Multimedia Experience, pp. 1–6, 21–23 (June 2010)

  10. Soundararajan, R., Bovik, A.C.: RRED indices: reduced reference entropic differencing for image quality assessment. IEEE Trans. Image Process. 21(2), 517–526 (2012)

    Article  MathSciNet  Google Scholar 

  11. Wang, Z., Bovik, A.C.: Reduced and no-reference image quality assessment: the natural scene statistic model approach. IEEE Signal Process. Mag. 28(6), 29–40 (2011)

    Article  Google Scholar 

  12. Foley, J.: Human luminence pattern mechanisms: masking experiments require a new model. J. Opt. Soc. Am. A 11(6), 1710–1719 (1994)

    Article  Google Scholar 

  13. Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society, Mathematical, physical and, engineering sciences, vol. 454, no. 1971, pp. 903–995 (1998)

  14. Ait Abdelouahad, A., El Hassouni, M., Cherifi, H., Aboutajdine, D.: Image quality assessment based on IMF coefficients modeling. The International Conference on Digital Information and Communication Technology and its Applications, CCIS 166 Springer, vol. 166, pp. 131–145, Dijon, France (2011)

  15. Le Callet, P., Autrusseau, F.: Subjective quality assessment irccyn/ivc database (2005). Retrieved from http://www.irccyn.ec-nantes.fr/ivcdb/

  16. Sheikh, H., Wang, Z., Cormack, L., Bovik, A.: LIVE image quality assessment database (2005). Retrieved from http://live.ece.utexas.edu/research/quality

  17. Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID2008: a database for evaluation of full-reference visual quality assessment metrics. Adv. Mod. Radioelectr. 10, 30–45 (2009)

    Google Scholar 

  18. Linderhed, A.: Variable sampling of the empirical mode decomposition of two dimensional signals. Int. J. Wavelets Multiresolut Inf. Process. 3(3), 435–452 (2005)

    Article  MATH  Google Scholar 

  19. Damerval, C., Meignen, S., Perrier, V.: A fast algorithm for bidimensional EMD. IEEE Signal Process. Lett. 12(10), 701–704 (2005)

    Article  Google Scholar 

  20. Bhuiyan, S., Adhami, R., Khan, J.: A novel approach of fast and adaptive bidimensional empirical mode decomposition. Speech and Signal Processing, IEEE International Conference on Acoustics, vol. 2008, no. 21, pp. 1313–1316. University of Alabama in Huntsville, USA (2008)

  21. Nunes, J., Bouaoune, Y., Delechelle, E., Niang, O., Bunel, P.: Image analysis by bidimensional empirical mode decomposition. Image Vis. Comput. 21(12), 1019–1026 (2003)

    Article  Google Scholar 

  22. Taghia, J., Doostari, M., Taghia, J.: An image watermarking method based on bidimensional empirical mode decomposition. Congress on Image and Signal Processing, vol. 5, pp. 674–678. IEEE Computer Society, Washington, DC, USA (2008)

  23. Andaloussi, J., Lamard, M., Cazuguel, G., Tairi, H., Meknassi, M., Cochener B., Roux, C.: Content based medical image retrieval: use of generalized Gaussian density to model BEMD IMF. World Congress on Medical Physics and Biomedical Engineering, vol. 25, no. 4, pp. 1249–1252. Munich, Germany (2009)

  24. Wan, J., Ren, L., Zhao, C.: Image feature extraction based on the two-dimensional empirical mode decomposition. Congress on Image and Signal Processing, vol. 1, pp. 627–631, Inf. Commun. Eng. Coll., Harbin Eng. Univ., Harbin (2008)

  25. Van de Wouwer, G., Scheunders, P., Van Dyck, D.: Statistical texture characterization from discrete wavelet representations. IEEE Trans. Image Process. 8(4), 592–598 (1999)

    Article  Google Scholar 

  26. De Forges, J.R.O.: Locally adaptive method for progressive still image coding. IEEE International Symposium on Signal Processing and its Applications, vol. 2, pp. 825–829. Brisbane, Australia (1999)

  27. Kodak Lossless True Color Image Suite (2007). http://r0k.us/graphics/kodak/

  28. VQEG: Final report from the video quality experts group on the validation of objective models of video quality assessment (2000). Retrieved from http://www.vqeg.org/

  29. Demirkesen C., Cherifi H.: A comparison of multiclass SVM methods for real world natural Scenes. Advanced Concepts for Intelligent vision Systems, vol. 2008, no. 5259, pp. 752–763. Juan-les-Pins, France (2008)

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Acknowledgments

The authors would like to thank Dr. H. R. Sheikh for supplying the LIVE image dataset password, Dr. Z. Wang for the Matlab routines used in VQEG Phase I FR-TV test for the regression analysis of subjective/objective data comparison.

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Authors and Affiliations

  1. LRIT, Unité Associée au CNRST (URAC 29), Université Mohammed V, Agdal, Morocco

    Abdelkaher Ait Abdelouahad & Driss Aboutajdine

  2. DESTEC, FLSHR, Université Mohammed V, Agdal, Morocco

    Mohammed El Hassouni

  3. Le2i-UMR CNRS 5158, University of Burgundy, Dijon, France

    Hocine Cherifi

Authors
  1. Abdelkaher Ait Abdelouahad
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  2. Mohammed El Hassouni
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  3. Hocine Cherifi
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  4. Driss Aboutajdine
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Correspondence to Abdelkaher Ait Abdelouahad.

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Ait Abdelouahad, A., El Hassouni, M., Cherifi, H. et al. Reduced reference image quality assessment based on statistics in empirical mode decomposition domain. SIViP 8, 1663–1680 (2014). https://doi.org/10.1007/s11760-012-0407-0

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