Blind image quality assessment via learnable attention-based pooling

doi:10.1016/j.patcog.2019.02.021

CASIA OpenIR > 模式识别国家重点实验室

	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	被引频次：27[WOS] [WOS记录] [WOS相关记录]
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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