A Deep Neural Network Based on Layer-by-Layer Fusion of Multi-Scale Features for No-Reference Image Quality Assessment

The existing deep network-based no-reference image quality assessment algorithms for authentic distortions have poor performance in representing the quality of natural scene images, which limited their evaluation accuracy and generalization ability. To solve this problem, this paper proposed a deep neural network based on fuse multi-scale features layer-by-layer (MsFF-Net). Firstly, the pre-trained ResNet-50 was used to extract multi-scale features of the image. Then, a multi-scale features fusion module was proposed, which gradually fused adjacent-scale features layer-by-layer to obtain multi-scale fused features that can accurately represent the image quality. The low-dimensional features were further extracted from the multi-scale fused features to obtain multi-granularity image quality perception features. Finally, regression was performed on the low-dimensional features by using a fully connected network which was adaptively generated by the highest-level features. The simulation results show that MsFF-Net outperforms most of the current methods on authentic distortions databases, and it achieves excellent performances on synthetic distortions databases.