Blind image quality assessment via learnable attention-based pooling

作者：

Highlights：

• We propose an attention-based pooling network for BIQA.

• A correlation constraint between the estimated local quality and attention weight in the network is introduced to regulate the training.

• An effective and interpretable attention model can be learned merely from subjective quality scores, without accessing to attention/saliency supervision.

• The learned attention-based pooling can be applied to other IQA metrics to help improve the performance.

摘要

•We propose an attention-based pooling network for BIQA.•A correlation constraint between the estimated local quality and attention weight in the network is introduced to regulate the training.•An effective and interpretable attention model can be learned merely from subjective quality scores, without accessing to attention/saliency supervision.•The learned attention-based pooling can be applied to other IQA metrics to help improve the performance.

论文关键词：Image quality assessment,Perceptual image quality,Visual attention,Convolutional neural network,Learnable pooling

论文评审过程：Received 20 April 2018, Revised 29 December 2018, Accepted 22 February 2019, Available online 23 February 2019, Version of Record 18 March 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.02.021