Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Blind image quality assessment via learnable attention-based pooling | |
Gu, Jie1,2; Meng, Gaofeng1; Xiang, Shiming1,2; Pan, Chunhong1 | |
发表期刊 | PATTERN RECOGNITION |
ISSN | 0031-3203 |
2019-07-01 | |
卷号 | 91页码:332-344 |
通讯作者 | Meng, Gaofeng(gfmeng@nlpr.ia.ac.cn) |
摘要 | Many recent algorithms based on convolutional neural network (CNN) for blind image quality assessment (BIQA) share a common two-stage structure, i.e., local quality measurement followed by global pooling. In this paper, we mainly focus on the pooling stage and propose an attention-based pooling network (APNet) for BIQA. The core idea is to introduce a learnable pooling that can model human visual attention in a data-driven manner. Specifically, the APNet is built by incorporating an attention module and allows for a joint learning of local quality and local weights. It can automatically learn to assign visual weights while generating quality estimations. Moreover, we further introduce a correlation constraint between the estimated local quality and attention weight in the network to regulate the training. The constraint penalizes the case in which the local quality estimation on a region attracting more attention differs a lot from the overall quality score. Experimental results on benchmark databases demonstrate that our APNet achieves state-of-the-art prediction accuracy. By yielding an attention weight map as by-product, our model gives a better interpretability on the learned pooling. (C) 2019 Elsevier Ltd. All rights reserved. |
关键词 | Image quality assessment Perceptual image quality Visual attention Convolutional neural network Learnable pooling |
DOI | 10.1016/j.patcog.2019.02.021 |
关键词[WOS] | PERCEPTUAL IMAGE ; VISUAL-ATTENTION ; SCORES |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Beijing Natural Science Foundation[L172053] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[61573352] ; Beijing Natural Science Foundation[L172053] |
项目资助者 | National Natural Science Foundation of China ; Beijing Natural Science Foundation |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000466250400027 |
出版者 | ELSEVIER SCI LTD |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23709 |
专题 | 模式识别国家重点实验室 |
通讯作者 | Meng, Gaofeng |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Gu, Jie,Meng, Gaofeng,Xiang, Shiming,et al. Blind image quality assessment via learnable attention-based pooling[J]. PATTERN RECOGNITION,2019,91:332-344. |
APA | Gu, Jie,Meng, Gaofeng,Xiang, Shiming,&Pan, Chunhong.(2019).Blind image quality assessment via learnable attention-based pooling.PATTERN RECOGNITION,91,332-344. |
MLA | Gu, Jie,et al."Blind image quality assessment via learnable attention-based pooling".PATTERN RECOGNITION 91(2019):332-344. |
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