Abstract
Generic image quality (IQ) metrics based on individual features are not capable of making accurate prediction across different distortion types. In this paper, we propose a two-stage scheme to overcome this limitation. At the first stage, the image distortion type is predicted by support-vector classifiers. At the second stage, decision-level fusion of three existing IQ metrics are conducted based on the k-nearest-neighbor (k-NN) regression where the acquired distortion-type knowledge is employed. When evaluated on the largest publicly-available IQ database which involves a large variety of distortion types, the proposed approach demonstrates impressive accuracy and robustness.
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
Okarma, K.: 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.) ICAISC 2010. LNCS, vol. 6113, pp. 539–546. Springer, Heidelberg (2010)
Larson, E.C., Chandler, D.M.: Most Apparent Distortion: Full-Reference Image Quality Assessment and the Role of Strategy. J. Electron. Imaging 19(1), 011006 (2010)
Falk, T.H., Guo, Y., Chan, W.Y.: Improving Robustness of Image Quality Measurement with Degradation Classification and Machine Learning. In: 41st Asilomar Conference on Signals, Systems and Computers, Pacific Grove, pp. 503–507 (2007)
Liu, M., Yang, X.: Image Quality Assessment by Decision Fusion. IEICE Electronics Express 5(15), 537–542 (2008)
Hsu, C., Lin, C.: A Comparison of Methods for Multiclass Support Vector Machines. IEEE Trans. on Neural Networks 13(2), 415–425 (2002)
MeTriX MuX Visual Quality Assessment Package, http://foulard.ece.cornell.edu/gaubatz/metrix_mux/
Chang, C.W., Lin, C.J.: LIBSVM: A Library for Support Vector Machines. ACM Trans. on Intell. Syst. Technol. 2(3), 27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Ponomarenko, N., Battisti, F., Egiazarian, K., Astola, J., Lukin, V.: Metrics Performance Comparison for Color Image Database. In: 4th International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale (2009)
Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID 2008 - A Database for Evaluation of Full-Reference Visual Quality Assessment Metrics. Advances of Modern Radioelectronics 10, 30–45 (2009)
Damera-Venkata, N., Kite, T.D., Geisler, W.S., Evans, B.L., Bovik, A.C.: Image Quality Assessment Based on a Degradation Model. IEEE Trans. on Image Process. 9(4), 636–650 (2000)
Mitsa, T., Varkur, K.L.: Evaluation of Contrast Sensitivity Functions for the Formulation of Quality Measures Incorporated in Halftoning Algorithms. In: 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing, Minneapolis, pp. 301–304 (1993)
Chandler, D.M., Hemami, S.S.: VSNR: A Wavelet-based Visual Signal-to-Noise Ratio for Natural Images. IEEE Trans. Image Process. 16(9), 2284–2298 (2007)
Sheikh, H.R., Bovik, A.C.: Image Information and Visual Quality. IEEE Trans. Image Process. 15(2), 43–444 (2006)
Sheikh, H.R., Bovik, A.C., de Veciana, G.: An Information Fidelity Criterion for Image Quality Assessment using Natural Scene Statistics. IEEE Trans. Image Process. 14(12), 2117–2128 (2005)
Wang, Z., Bovik, A.C.: A Universal Image Quality Index. IEEE Signal Process. Letters 9(3), 81–84 (2002)
Lin, W., Kuo, C.C.J.: Perceptual Visual Quality Metrics: A Survey. Journal of Visual Communication and Image Representation 22(4), 297–312 (2011)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image Quality Assessment: From Error Measurement to Structural Similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-Scale Structural Similarity for Image Quality Assessment. In: Proc. 37th IEEE Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Peng, P., Li, Z. (2012). Image Quality Assessment Based on Distortion-Aware Decision Fusion. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_82
Download citation
DOI: https://doi.org/10.1007/978-3-642-31919-8_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
eBook Packages: Computer ScienceComputer Science (R0)