Abstract
A novel image quality metric based on the characteristics of wavelet coefficients of images is proposed in this paper. An image is decomposed into several levels by means of wavelet transform. The standard deviations of the diagonal details (HH coefficients) at each level increase with the noise standard deviation increasing and decrease with the blurring radius increasing. According to that, an image quality can be measured by analyzing the characteristics of its wavelet coefficients. Neural network is used to realize the algorithm of image quality assessment. The results of experiments demonstrate that the image quality metric is reasonable and the algorithm realization using neural network is feasible and performs well.
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Yue, D., Huang, X., Tan, H. (2007). Image Quality Assessment Based on Wavelet Coefficients Using Neural Network. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_105
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DOI: https://doi.org/10.1007/978-3-540-72395-0_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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