Wavelet-based hybrid natural image modeling using generalized Gaussian and α-stable distributions |
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| Affiliation: | 1. Dept. of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan, ROC;2. Research Center for Information Technology Innovation, Academia Sinica, Taiwan, ROC;3. Graduate Institute of Communication Engineering, National Chung Hsing University, Taichung, Taiwan, ROC;1. College of Information Science and Engineering, Ocean University of China, Qingdao, China;2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;3. Department of Electrical Engineering, Princeton University, NJ 08544, USA;4. Department of Automation, Tsinghua University, Beijing, China;5. Department of Computer Science, Tsinghua University, Beijing, China;6. Department of Computer, Shandong University, Weihai, China;1. National Engineering School of Monastir, University of Monastir, Tunisia;2. Department of Computer Engineering, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia |
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| Abstract: | Natural image is characterized by its highly kurtotic and heavy-tailed distribution in wavelet domain. These typical non-Gaussian statistics are commonly described by generalized Gaussian density (GGD) or α-stable distribution. However, each of the two models has its own deficiency to capture the variety and complexity of real world scenes. Considering the statistical properties of GGD and α-stable distributions respectively, in this paper we propose a hybrid statistical model of natural image’s wavelet coefficients which is better in describing the leptokurtosis and heavy tails simultaneously. Based on a clever fusion of GGD and α-stable functions, we establish the optimal parametric hybrid model, and a close-formed Kullback–Leibler divergence of the hybrid model is derived for evaluating model accuracy. Experiment results and comparative studies demonstrate that the proposed hybrid model is closer to the true distribution of natural image’s wavelet coefficients than the single modeling using GGD or α-stable, while is beneficial for applications such as image comparison. |
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| Keywords: | Natural image statistics Image modeling Wavelet coefficients Generalized Gaussian distribution Hybrid model Kullback–Leibler divergence Image comparison |
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