Abstract
The available blind image quality assessment (BIQA) criteria usually involve a large amount of human scored images to train a regression model used to judge image quality, which makes the results are closely related to the amount of training data. In this paper, a valid BIQA algorithm based on shearlet transform without using human scored images is presented. This is mainly based on that degradation of image induces considerable deviation in the distributed discontinuities in different directions. However, the distributed discontinuities can be localized by shearlet transform effectively. The nature scene statistics (NSS) of shearlet coefficients are capable for exhibiting the alteration of image quality. Experimental results on two benchmarking databases (LIVEII and TID2008) indicate the rationality and validity of the proposed method.
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Acknowledgments
This research was supported partially by the National Natural Science Foundation of China (No. 61125204, 61372130, and 61432014), the Fundamental Research Funds for the Central Universities (No. BDY081426, and JB140214), the Program for New Scientific and Technological Star of Shaanxi Province (No. 2014KJXX-47), and the Project Funded by China Postdoctoral Science Foundation (No. 2014M562378).
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Ren, Y., Lu, W., He, L., Gao, X. (2015). Blind Image Quality Assessment in Shearlet Domain. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_48
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DOI: https://doi.org/10.1007/978-3-319-23989-7_48
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