An End-to-End Blind Image Quality Assessment Method Using a Recurrent Network and Self-Attention

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

10 Scopus Citations
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Author(s)

  • Mingliang Zhou
  • Xuting Lan
  • Qin Mao
  • Yutong Li
  • Chao Wu
  • Tao Xiang
  • Bin Fang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)369-377
Number of pages9
Journal / PublicationIEEE Transactions on Broadcasting
Volume69
Issue number2
Online published28 Oct 2022
Publication statusPublished - Jun 2023

Abstract

In this paper, we propose a blind image quality assessment (BIQA) method using self-attention and a recurrent neural network (RNN); this approach can effectively capture both local and global information from an input image. The implementation of our constructed deep no-reference (NR) assessment framework does not rely on any convolutional operations. First, the capture step for obtaining locally significant information is performed by a self-attention operation inside a divided window. Second, we design a serialized feature input memory subnetwork to fuse the global features of the image. Finally, all the integrated features are uniformly mapped to the target score. The experimental results obtained on publicly available benchmark IQA databases show that our approach outperforms other state-of-the-art algorithms.

Research Area(s)

  • Blind image quality assessment, Deep learning, Distortion, Feature extraction, Image quality, recurrent neural network, self-attention, Task analysis, Transformers, Visualization

Citation Format(s)

An End-to-End Blind Image Quality Assessment Method Using a Recurrent Network and Self-Attention. / Zhou, Mingliang; Lan, Xuting; Wei, Xuekai et al.
In: IEEE Transactions on Broadcasting, Vol. 69, No. 2, 06.2023, p. 369-377.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review