Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 27, Issue 1, Pages 206-219Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2017.2760518
Keywords
Full-reference image quality assessment; no-reference image quality assessment; neural networks; quality pooling; deep learning; feature extraction; regression
Funding
- German Ministry for Education and Research as Berlin Big Data Center [01IS14013A]
- Institute for Information and Communications Technology Promotion through the Korea Government [2017-0-00451]
- DFG
- National Research Foundation of Korea through Ministry of Education, Science, and Technology
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We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features.
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