Attention-Enhanced Feature Fusion Network for No-Reference Image Quality Assessment

Abstract

No-Reference Image Quality Assessment (NR-IQA) is a fundamental computer vision task. In this paper, we propose a new Attention-Enhanced Feature Fusion Network for NR-IQA (AEFF-IQA) that integrates multi-scale local and non-local features. Firstly, multi-scale local and non-local features containing distortion information at semantic level are extracted by a feature extraction module consisting of two pre-trained deep neural networks. Secondly, a self-attention enhanced fusion module with four self-attention enhanced fusion components fuses the local and non-local features at the same scale to obtain multi-scale fusion features. Then, a cross-attention enhanced fusion module containing three cross-attention enhanced fusion components integrates these fusion features across multiple scales. Finally, the quality score is obtained by using a prediction module. Extensive experiments are conducted on five public datasets. The results show that our AEFF-IQA outperforms some state-of-the-art models and exhibits good generalization performance.