QL-IQA: Learning distance distribution from quality levels for blind image quality assessment

作者：

Highlights：

• We propose NR-IQA by learning distance distribution from quality levels.

• The pseudo Siamese network is employed to learn the distance distribution.

• A fusion layer is proposed to average the quality scores learnt by the Siamese network.

• Cross-database evaluation verifies high generalization ability and effectiveness of our model.

摘要

•We propose NR-IQA by learning distance distribution from quality levels.•The pseudo Siamese network is employed to learn the distance distribution.•A fusion layer is proposed to average the quality scores learnt by the Siamese network.•Cross-database evaluation verifies high generalization ability and effectiveness of our model.

论文关键词：No-reference image quality assessment,Pseudo Siamese network,Clustering,Convolutional neural network

论文评审过程：Received 6 January 2021, Revised 8 September 2021, Accepted 3 November 2021, Available online 24 November 2021, Version of Record 29 November 2021.

论文官网地址：https://doi.org/10.1016/j.image.2021.116576