Deep Blind Synthesized Image Quality Assessment with Contextual Multi-Level Feature Pooling

Wang, Xiaochuan; Wang, Kai; Yang, Bailin; Li, Frederick W.B.; Liang, Xiaohui

Abstract

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.

Citation

Wang, X., Wang, K., Yang, B., Li, F. W., & Liang, X. (2019, December). Deep Blind Synthesized Image Quality Assessment with Contextual Multi-Level Feature Pooling. Presented at 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2019 IEEE International Conference on Image Processing (ICIP)
• Acceptance Date: Apr 30, 2019
• Online Publication Date: Aug 26, 2019
• Publication Date: Aug 26, 2019
• Deposit Date: Oct 30, 2019
• Publicly Available Date: Oct 30, 2019
• Pages: 435-439
• Series ISSN: 2381-8549
• Book Title: 2019 IEEE International Conference on Image Processing Proceedings.
• DOI: https://doi.org/10.1109/icip.2019.8802943
• Public URL: https://durham-repository.worktribe.com/output/1141634
• Related Public URLs: https://ieeexplore.ieee.org/document/8802943