期刊
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
卷 11, 期 1, 页码 206-220出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTSP.2016.2639328
关键词
Convolutional neural network; deep learning; image quality assessment; no-reference image quality assessment
资金
- National Research Foundation of Korea (NRF) grant - Korea government (MSIP) [2016R1A2B2014525]
- National Research Foundation of Korea [2016R1A2B2014525] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
In general, owing to the benefits obtained from original information, full-reference image quality assessment (FR-IQA) achieves relatively higher prediction accuracy than no-reference image quality assessment (NR-IQA). By fully utilizing reference images, conventional FR-IQA methods have been investigated to produce objective scores that are close to subjective scores. In contrast, NR-IQA does not consider reference images; thus, its performance is inferior to that of FR-IQA. To alleviate this accuracy discrepancy between FR-IQA and NR-IQA methods, we propose a blind image evaluator based on a convolutional neural network (BIECON). To imitate FR-IQA behavior, we adopt the strong representation power of a deep convolutional neural network to generate a local quality map, similar to FR-IQA. To obtain the best results from the deep neural network, replacing hand-crafted features with automatically learned features is necessary. To apply the deep model to the NR-IQA framework, three critical problems must be resolved: 1) lack of training data; 2) absence of local ground truth targets; and 3) different purposes of feature learning. BIECON follows the FR-IQA behavior using the local quality maps as intermediate targets for conventional neural networks, which leads to NR-IQA prediction accuracy that is comparable with that of state-of-the-art FR-IQA methods.
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