Yan Fu
College of Computer Science and Technology, Xi`an University of Science and Technology, 710054, Xi`an Shaanxi, China
Danting Xie
College of Electrical and control engineering, Xi`an University of Science and Technology, 710054, Xi`an Shaanxi, China
ABSTRACT
Considering that many no reference image quality assessment methods cannot give better assessment results for blurred images, this study proposes a no-reference image quality assessment method which has better assessment results. The method firstly extracts texture and structure features of a blurred image and then calculates its texture similarity and structural similarity. Finally, with these texture similarity and structural similarity as the input factors, the subjective assessment value DMOS provided by LIVE database as the output factor, a [2 9 1] Back-Propagation (BP) neural network prediction model is built. The experimental results show that the prediction model is stable and the differences between subjective assessment values and prediction results are small. Their Correlation Coefficients are all above 0.9.
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How to cite this article
Yan Fu and Danting Xie, 2013. No Reference Image Quality Assessment Method for Blurred Image. Information Technology Journal, 12: 3349-3352.
DOI: 10.3923/itj.2013.3349.3352
URL: https://scialert.net/abstract/?doi=itj.2013.3349.3352
DOI: 10.3923/itj.2013.3349.3352
URL: https://scialert.net/abstract/?doi=itj.2013.3349.3352
REFERENCES
- Baraldi, A. and F. Parmiggiani, 1995. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Trans. Geosci. Remote Sensing, 33: 293-304.
CrossRef - Fan, Y.Y., Y.J. Sang and X.H. Shen, 2011. Optimization of image quality assessment parameters based on back-propagation neural network. J. Applied Opt., 32: 1150-1155.
Direct Link - Haralick, R.M., K. Shanmugam and I.H. Dinstein, 1973. Textural features for image classification. IEEE Trans. Syst. Man Cybern., SMC-3: 610-621.
CrossRefDirect Link - Wang, Z., A.C. Bovik, H.R. Sheikh and E.P. Simoncelli, 2004. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process., 13: 600-612.
CrossRefDirect Link - Xie, X.F., J. Zhou and Q.Z. Wu, 2010. No-reference quality index for image blur. J. Comput. Appl., 30: 921-924.
Direct Link - Zhang, T., D.Q. Liang, X.N. Wang and X. Zhang, 2012. Novel texture features based no-reference image blur metric. Comput. Eng. Appl., 48: 185-191.
Direct Link - Zhou, J.C., R.W. Dai and B.H. Xiao, 2008. Overview of image quality assessment research. Comput. Sci., 35: 1-4.
CrossRefDirect Link