Aiming to enhance the prediction accuracy of image quality generated by GAN model to better align with human subjective assessment of image quality,in this study,a semi-supervised Image Quality Assessment(IQA)method based on knowledge distillation was introduced.This method combined CNN and ViT models to fully capture the global and local information.The distribution differences of features between high-quality images and distorted images were learned and the advanced feature information was transferred through knowledge distillation.Image quality assessment scores were obtained through forward propagation.To increase the diversity of input features and improve the processing speed of the model a Cascaded Group Attention(CGA)mechanism was employed for input feature processing.Experimental results on multiple public datasets demonstrated that this method outperforms existing evaluation methods,yielding overall favorable outcomes,exhibiting relatively robust performance and can achieve IQA results that better align with human visual perception.
Knowledge distillationGAN modelImage quality assessmentCascaded Group Attention
NETL
NSTL
万方数据
