Blind Image Quality Assessment Method Based on DeepSA-Net

Blind image quality assessment refers to the accurate prediction of the visual quality of any input image without a reference image. With the rapid growth of the number of images and increasing requirements for image quality, how to assess image quality has become an urgent problem. Complex images are difficult to consider professionally from a single perspective. A blind image quality assessment algorithm based on a deep semantic adaptation network (DeepSA-Net) is proposed. Based on the end-to-end deep learning model, the semantic pre-trained models and multi-resolution adaptive module are added. The adaptive factor \(\alpha \) is proposed to better capture global and local quality information and fuse multi-resolution features to improve the convergence ability and speed of the network. Finally, the quality assessment results of images are obtained by regression. The experiment used the Spearman correlation coefficient and Pearson correlation coefficient as assessment indicators. The results showed that DeepSA-Net outperformed most current methods in real distortion scene databases and had excellent assessment ability in synthetic distortion databases. In addition, ablation study and different distortion studies were designed to fully validate the effectiveness and feasibility of the algorithm.