Abstract
Reduced reference image quality assessment (RRIQA) methods aim to assess the quality of a perceived image with only a reduced cue from its original version, called ”reference image”. The powerful advantage of RR methods is their ”General-purpose”. However, most introduced RR methods are built upon a non-adaptive transform models. This can limit the scope of RR methods to a small number of distortion types. In this work, we propose a bi-dimensional empirical mode decomposition-based RRIQA method. First, we decompose both, reference and distorted images, into Intrinsic Mode Functions (IMF), then we use the Generalized Gaussian Density (GGD) to model IMF coefficients. Finally, the distortion measure is computed from the ”fitting errors”, between the empirical and the theoretical IMF histograms, using the Kullback Leibler Divergence (KLD). In order to evaluate the performance of the proposed method, two approaches have been investigated : the logistic function-based regression and the well known Support vector machine-based classification. Experimental results show a high correlation between objective and subjective scores.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
UIT-R Recommendation BT. 500-10,Méthodologie d’évaluation subjective de la qualité des images de télévision. tech. rep., UIT, Geneva, Switzerland (2000)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 1624–1639 (2004)
Wang, Z., Sheikh, H.R., Bovik, A.C.: No-reference perceptual quality assessment of JPEG compressed images. In: IEEE International Conference on Image Processing, pp. 477–480 (2002)
Gunawan, I.P., Ghanbari, M.: Reduced reference picture quality estimation by using local harmonic amplitude information. In: Proc. London Commun. Symp., pp. 137–140 (September 2003)
Kusuma, T.M., Zepernick, H.-J.: A reduced-reference perceptual quality metric for in-service image quality assessment. In: Proc. Joint 1st Workshop Mobile Future and Symp. Trends Commun., pp. 71–74 (October 2003)
Carnec, M., Le Callet, P., Barba, D.: An image quality assessment method based on perception of structural information. In: Proc. IEEE Int. Conf. Image Process., vol. 3, pp. 185–188 (September 2003)
Carnec, M., Le Callet, P., Barba, D.: Visual features for image quality assessment with reduced reference. In: Proc. IEEE Int. Conf. Image Process., vol. 1, pp. 421–424 (September 2005)
Wang, Z., Simoncelli, E.: Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In: Proc. of SPIE Human Vision and Electronic Imaging, pp. 149–159 (2005)
Foley, J.: Human luminence pattern mechanisms: Masking experiments require a new model. J. of Opt. Soc. of Amer. A 11(6), 1710–1719 (1994)
Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and the hilbert spectrum for non-linear and non-stationary time series analysis. Proc. Roy. Soc. Lond. A,. 454, 903–995 (1998)
Nunes, J., Bouaoune, Y., Delechelle, E., Niang, O., Bunel, P.: Image analysis by bidimensional empirical mode decomposition. Image and Vision Computing 21(12), 1019–1026 (2003)
Taghia, J., Doostari, M., Taghia, J.: An Image Watermarking Method Based on Bidimensional Empirical Mode Decomposition. In: Congress on Image and Signal Processing (CISP 2008), pp. 674–678 (2008)
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. In: World Congress on Medical Physics and Biomedical Engineering, vol. 25(4), pp. 1249–1252 (2009)
Wan, J., Ren, L., Zhao, C.: Image Feature Extraction Based on the Two-Dimensional Empirical Mode Decomposition. In: Congress on Image and Signal Processing, CISP 2008, vol. 1, pp. 627–631 (2008)
Linderhed, A.: Variable sampling of the empirical mode decomposition of twodimensional signals. Int. J. Wavelets Multresolution Inform. Process. 3, 435–452 (2005)
Damerval, C., Meignen, S., Perrier, V.: A fast algorithm for bidimensional EMD. IEEE Sig. Process. Lett. 12, 701–704 (2005)
Bhuiyan, S., Adhami, R., Khan, J.: A novel approach of fast and adaptive bidimensional empirical mode decomposition. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 (ICASSP 2008), pp. 1313–1316 (2008)
Van de Wouwer, G., Scheunders, P., Van Dyck, D.: Statistical texture characterization from discrete wavelet representations. IEEE transactions on image processing 8(4), 592–598 (1999)
Sheikh, H., Wang, Z., Cormack, L., Bovik, A.: LIVE image quality assessment database. 2005-2010), http://live.ece.utexas.edu/research/quality
Rohaly, A., Libert, J., Corriveau, P., Webster, A., et al.: 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
Demirkesen, C., Cherifi, H.: A comparison of multiclass SVM methods for real world natural scenes. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 752–763. Springer, Heidelberg (2008)
Saad, M., Bovik, A.C., Charrier, C.: A DCT statistics-based blind image quality index. IEEE Signal Processing Letters, 583–586 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ait Abdelouahad, A., El Hassouni, M., Cherifi, H., Aboutajdine, D. (2011). Image Quality Assessment Based on Intrinsic Mode Function Coefficients Modeling. In: Cherifi, H., Zain, J.M., El-Qawasmeh, E. (eds) Digital Information and Communication Technology and Its Applications. DICTAP 2011. Communications in Computer and Information Science, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21984-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-21984-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21983-2
Online ISBN: 978-3-642-21984-9
eBook Packages: Computer ScienceComputer Science (R0)