Abstract
Research on no-reference image quality assessment (IQA) aims to develop a computational model simulating the human perception of image quality accurately and automatically without any prior information about the reference clean image signals. In this paper, we introduce a novel no-reference IQA metric, based on the analysis of structural degradation and luminance changes. Since the human visual system (HVS) is highly sensitive to structural distortion, we encode the image structural information as the local binary pattern (LBP) distribution. Besides, image quality is also affected by luminance changes, which cannot be captured properly by LBP threshold mechanism. Hence, the distribution of normalized luminance magnitudes is also included in the proposed IQA metric. Extensive experiments conducted on two large public image databases have demonstrated the effectiveness and robustness of the proposed metric in comparison with the relevant state-of-the-art metrics.
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This work was funded by the Ph.D. Grant from the Institute for Media Innovation, Nanyang Technological University, Singapore.
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Li, Q., Lin, W., Xu, J., Fang, Y., Thalmann, D. (2016). No-reference Image Quality Assessment Based on Structural and Luminance Information. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_25
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DOI: https://doi.org/10.1007/978-3-319-27671-7_25
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