CASIA OpenIR  > 模式识别国家重点实验室
Blind Image Quality Assessment via Learnable Attention-based Pooling
Jie, Gu1,2; Gaofeng, Meng1; Shiming Xiang1,2; Chunhong, Pan1
Source PublicationPattern Recognition
2019
Volume91Issue:1Pages:332-344
Abstract

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.

KeywordImage Quality Assessment Perceptual Image Quality Visual Attention Convolutional Neural Network Learnable Pooling
Indexed BySCI
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23709
Collection模式识别国家重点实验室
Corresponding AuthorGaofeng, Meng
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jie, Gu,Gaofeng, Meng,Shiming Xiang,et al. Blind Image Quality Assessment via Learnable Attention-based Pooling[J]. Pattern Recognition,2019,91(1):332-344.
APA Jie, Gu,Gaofeng, Meng,Shiming Xiang,&Chunhong, Pan.(2019).Blind Image Quality Assessment via Learnable Attention-based Pooling.Pattern Recognition,91(1),332-344.
MLA Jie, Gu,et al."Blind Image Quality Assessment via Learnable Attention-based Pooling".Pattern Recognition 91.1(2019):332-344.
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