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|Title:||Blind image quality assessment with hierarchy : degradation from local structure to deep semantics||Authors:||Wu, Jinjian
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2019||Source:||Wu, J., Zeng, J., Dong, W., Shi, G. & Lin, W. (2019). Blind image quality assessment with hierarchy : degradation from local structure to deep semantics. Journal of Visual Communication and Image Representation, 58, 353-362. https://dx.doi.org/10.1016/j.jvcir.2018.12.005||Journal:||Journal of Visual Communication and Image Representation||Abstract:||Though blind image quality assessment (BIQA) is highly desired in perceptual-oriented image processing systems, it is extremely difficult to design a reliable BIQA method. With the help of the prior knowledge, the human visual system (HVS) hierarchically perceives the quality degradation during the visual recognition. Inspired by this, we suggest different levels of distortion generate individual degradations on hierarchical features, and propose to consider the degradations on both low and high level features for quality prediction. By mimicking the orientation selectivity (OS) mechanism in the primary visual cortex, an OS based local structure is designed for low-level visual information representation. At the meantime, the deep residual network, which possesses multiple levels for feature integration, is employed to extract the deep semantics for high-level visual content representation. By fusing the local structure and the deep semantics, a hierarchical feature set is acquired. Next, the correlations between the degradations of image qualities and their corresponding hierarchical feature sets are analyzed, and a novel hierarchical feature degradation (HFD) based BIQA (HFD-BIQA) method is built. Experimental results on the legacy and wild image quality assessment databases demonstrate the prediction accuracy of the proposed HFD-BIQA method, and verify that the HFD-BIQA performs highly consistent with the subjective perception.||URI:||https://hdl.handle.net/10356/151370||ISSN:||1047-3203||DOI:||10.1016/j.jvcir.2018.12.005||Rights:||© 2018 Elsevier Inc. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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