Abstract
Image Quality Assessment(IQA) is of fundamental importance to numerous imaging and video processing applications. For most of the applications, the perceptual meaningful measure is the one which can automatically assess the quality of images or videos in a perceptually consistent manner. However, most commonly used IQA metrics are not consistent well with the human judgments of image quality. Recently, the SSIM metric which takes people’s visual characteristics into consideration performs much better than the traditional PSNR/MSE. But the defects of it still exit on some specific kinds of distortions. A new algorithm of IQA based on feature selection is proposed in this paper. Local gradient entropy and phase congruency are added to the SSIM framework. Through in-depth feature selection and definition plus better pooling strategy, this algorithm performs much better in LIVE datasets.
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Wang, Z., et al.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)
Tang, H., Joshi, N., Kapoor, A.: Learning a blind measure of perceptual image quality. In: Computer Vision and Pattern Recognition. IEEE Computer Society Press, Colorado Springs (2011)
Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing 15(11), 3440–3451 (2006)
Gabarda, S., Cristóbal, G.: Blind image quality assessment through anisotropy. Virtual Journal for Biomedical Optics 24(12), B42–B51 (2007)
Sheikh, H.R., Bovik, A.C., Cormack, L.: Blind quality assessment of JPEG 2000 compressed images using natural scene statistics. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers (2003)
Saad, M.A., Bovik, A.C., Charrier, C.: A DCT statistics-based blind image quality index. IEEE Signal Processing Letters 17(6), 583–586 (2010)
Lee, C., et al.: Objective video quality assessment. Optical Engineering 45(1), 17004-11 (2006)
Al-Hinai, N., et al.: Optimum wavelet thresholding based on structural similarity quality assessment for FFT-OFDM. In: International Conference on Advanced Technologies for Communications, ATC 2008 (2008)
Damera-Venkata, N., et al.: Image quality assessment based on a degradation model. IEEE Transactions on Image Processing 9(4), 636–650 (2000)
Chandler, D.M., Hemami, S.S.: VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images. IEEE Transactions on Image Processing 16(9), 2284–2298 (2007)
Chun-Ling, Y., Hua-Xing, W., Lai-Man, P.: Improved Inter Prediction based on Structural Similarity in H.264. In: IEEE International Conference on Signal Processing and Communications, ICSPC 2007 (2007)
Ho-Sung, H., Dong, O.K., Rae-Hong, P.: Structural information-based image quality assessment using LU factorization. IEEE Transactions on Consumer Electronics 55(1), 165–171 (2009)
Xinbo, G., et al.: Image Quality Assessment Based on Multiscale Geometric Analysis. IEEE Transactions on Image Processing 18(7), 1409–1423 (2009)
Bin, L., Yan, C.: An Image Quality Assessment Algorithm Based on Dual-scale Edge Structure Similarity. In: Second International Conference on Innovative Computing, Information and Control, ICICIC 2007 (2007)
Chen, G.-H., Yang, C.-L., Xie, S.-L.: Gradient-based structural similarity for image quality assessment. IEEE Computer Society, Atlanta (2006)
Huang, S., Burnett, T.J.W., Deczky, A.G.: The Importance of Phase in Image Processing Filters. IEEE Transactions on Acoustics, Speech, and Signal Processing (1975)
Oppenheim, A.V., Lim, J.S.: The Importance of Phase in Signals. Proceedings of the IEEE 69, 529–541 (1981)
Liu, Z., Laganire, R.: Phase congruence measurement for image similarity assessment. Pattern Recognition Letters 28(1), 166–172 (2007)
Szilagyi, T., Brady, S.M.: Feature extraction from cancer images using local phase congruency: A reliable source of image descriptors. IEEE Computer Society, Boston (2009)
Sheikh, H.R., et al.: LIVE Image Quality Assessment Database Release 2, http://live.ece.utexas.edu/research/quality
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Lu, T., Zhang, Y., Li, H. (2013). An Image Quality Assessment Algorithm Based on Feature Selection. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_36
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DOI: https://doi.org/10.1007/978-3-642-36669-7_36
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