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Deep blind wynthesized image quality assessment with contextual multi-level feature pooling.

Wang, Xiaochuan and Wang, Kai and Yang, Bailin and Li, Frederick W.B. and Liang, Xiaohui (2019) 'Deep blind wynthesized image quality assessment with contextual multi-level feature pooling.', in 2019 IEEE International Conference on Image Processing Proceedings. Piscataway, NJ: IEEE, pp. 435-439.


Blind image quality metrics have achieved significant improvement on traditional 2D image dataset, yet still being insufficient for evaluating synthesized images generated from depth-image-based rendering. The geometric distortions in synthesized image are non-uniform, which is challenging for feature representation and pooling. To address this, we propose an end-to-end deep blind synthesized image quality metric SIQA-CFP. We particularly design a contextual multilevel feature pooling module to encode low- and high-level features, which are extracted by a deep pre-trained ResNet. Experimental results on IRCCyN/IVC DIBR dataset show that our method outperforms state-of-the-art synthesized image quality metrics. Our method also achieves competitive performance on traditional 2D image datasets like LIVE Challenge and TID2013.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:30 April 2019
Date deposited:30 October 2019
Date of first online publication:26 August 2019
Date first made open access:30 October 2019

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