Distortion classification is an important step in blind image quality assessment. In this paper, a new image distortion classification algorithm is presented. Classification is based on features extracted from the distribution of the first digit of transform coefficients of the image. The generalized Benford’s law is used to model the distribution. The discrete cosine transform with three different patch sizes and the wavelet transform have been tested. Features, such as distribution data and model parameters, are extracted from an image. A kernel support vector machine is trained using these features. The LIVE database is used for both training and testing, while other four databases, namely, TID2008, CSIQ, Waterloo exploration database and McGill calibrated colour image database, are used for validation. Experimental results show that the performance of the proposed algorithm outperforms state-of-the-art algorithms in terms of classification accuracy.
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Hussein Al-Bandawi has been supported by the Higher Committee for Eduction Development in Iraq. The authors thank the reviewers for providing critical and constructive comments.
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Al-Bandawi, H., Deng, G. Classification of image distortion based on the generalized Benford’s law. Multimed Tools Appl 78, 25611–25628 (2019). https://doi.org/10.1007/s11042-019-7668-3
- Generalized Benford’s law
- Image distortion classification