Abstract
Our eyes receive the information of the images containing both the luminance information and chrominance information. However, the available blind image quality assessment (BIQA) criteria usually involve luminance information only. In this paper, we propose a novel efficient IQA metric via statistics of color descriptors in the DCT domain. Firstly, we calculate the saturation (S), hue (H), luminance (L) of the testing image simultaneously. Then the local DCT transform is implemented on each color descriptor, and the nature scene statistics (NSS) are extracted from the DCT coefficients. This is mainly based on the fact that the degradation of the image induces considerable deviation in the frequency domain characteristics of chromatic data in natural image. However, the deviation can be quantified by the DCT coefficients of the image’s color descriptors effectively. Finally, we construct the mapping relation between the features and the image quality. Experimental results on several benchmarking databases (TID2013, LIVEII and CSIQ) show the proposed method is superior to other state-of-the-arts methods and reveal the rationality and the validity of the new approach.
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Acknowledgments
This research was supported partially by the National Natural Science Foundation of China (Nos. 61372130, 61432014, 61501349, 61571343).
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Lin, B., Lu, W., He, L., Gao, X. (2017). Blind Image Quality Assessment: Using Statistics of Color Descriptors in the DCT Domain. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_5
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DOI: https://doi.org/10.1007/978-3-319-67777-4_5
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