Abstract
In this paper, we have proposed a novel image quality evaluation algorithm based on the Visual Difference Predictor(VDP), a classical method of estimating the visual similarity between an image under test and its reference one. Compared with state-of-the-art image quality evaluation algorithms, this method have employed a genetic algorithm based support vector machine, instead of linear or nonlinear mathematical models, to describe the relationship between image similarity features and subjective image quality. Subsequent experiments shows that, the proposed method with the state-of-the-art image quality evaluation algorithms the Mean Square Error (MSE), the Structural SImilarity Metric (SSIM), the Multi-scale SSIM (MS-SSIM). Experiments show that VDQM performs much better than its counterparts on both the LIVE and the A57 image databases.
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© 2011 Springer-Verlag Berlin Heidelberg
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Cui, L., Xie, S. (2011). The Application of Genetic Algorithm Based Support Vector Machine for Image Quality Evaluation. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21090-7_27
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DOI: https://doi.org/10.1007/978-3-642-21090-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21089-1
Online ISBN: 978-3-642-21090-7
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