Perceiving Multi-Layer Representations for No-reference Image Quality Assessment

In this paper, we propose an end-to-end no-reference image quality assessment (NR-IQA) method that perceives multilayer representations from low-level to high-level stages. First, multi-layer representations (MR) of the distorted images are extracted from different layers of multiple feature extraction networks to obtain fine-grained information. Second, a gated recurrent unit (GRU)-based fusion encoder (GFE) is presented to model the interrelationships between multi-layer representations, thereby generating the global feature. Finally, we construct a perception-oriented quality regression network (PQRN) to generate the quality scores. Experimental results on commonly used benchmark datasets verify the effectiveness of our proposed method over existing state-of-the-art approaches by a large margin.