Abstract
Intrinsic nonlinearity complicates the modeling of perceived quality of digital images, especially when using feature-based objective methods. The research described in this paper indicates that models from Computational Intelligence can predict quality and cope with multi-dimensional data characterized by complex perceptual relationships. A reduced-reference scheme exploits Support Vector Machines (SVMs) to assess the degradation in perceived image quality induced by three different distortion types: JPEG compression, white noise, and Gaussian blur. First, an objective description of the images is obtained by exploiting the co-occurrence matrix and its features; then, the SVM supports the nonlinear mapping between the objective description and the quality evaluation. Experimental results confirm the validity of the approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Wang, Z., Sheikh, H.R., Bovik, A.C.: Objective video quality assessment. In: Furth, B., Marques, O. (eds.) The Handbook of Video Databases: Design and Applications. CRC Press, Boca Raton (2003)
International Telecommunication Union: Methodology for the subjective assessment of the quality of television pictures (1995)
Wang, Z., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithm. IEEE Trans. Image Processing 15, 3441–3452 (2006)
Yeh, E.M., Kokaram, A.C., Kingsbury, N.G.: Perceptual distortion measure for edgelike artifacts in image sequences. In: Proc. The 3rd International Conference on Human Vision and Electronic Imaging, SPIE, pp. 160–172 (1998)
Karunasekera, S.A., Kingsbury, N.G.: A distortion measure for blocking artifacts in images based on human visual sensitivity. IEEE Trans. Image Processing 4, 713–724 (1995)
Wang, Z., Simoncelli, E.P.: Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In: Proc. SPIE Human Vision and Electronic Imaging X, vol. 5666, pp. 149–159 (2005)
Gastaldo, P., Zunino, R., Heynderickx, I., Vicario, E.: Objective quality assessment of displayed images by using neural networks. Signal Processing: Image Communication 20, 643–661 (2005)
Gastaldo, P., Zunino, R.: Neural networks for the no-reference assessment of perceived quality. Journal of Electronic Imaging 14, 033004/1–11 (2005)
Gastaldo, P., Parodi, G., Redi, J., Zunino, R.: No-reference quality assessment of JPEG images by using cbp neural networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4669, pp. 564–572. Springer, Heidelberg (2007)
Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. On Systems, Man and Cybernetics SMC-3, 610–621 (1973)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: Live image quality assessment database, http://live.ece.utexas.edu/research/quality
Bartlett, P., Boucheron, S., Lugosi, G.: Model selection and error estimation. Machine Learning 48, 85–113 (2002)
Anguita, D., Ridella, S., Rivieccio, F., Zunino, R.: Hyperparameter tuning criteria for support vector classifiers. Neurocomputing 55, 109–134 (2003)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence 20, 226–239 (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Redi, J., Gastaldo, P., Zunino, R., Heynderickx, I. (2008). Co-occurrence Matrixes for the Quality Assessment of Coded Images. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_92
Download citation
DOI: https://doi.org/10.1007/978-3-540-87536-9_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87535-2
Online ISBN: 978-3-540-87536-9
eBook Packages: Computer ScienceComputer Science (R0)