基于RFB和超网络的跨尺度多层次真实失真图像质量评价方法OA北大核心CSTPCD

An innovative dual-branch feature extraction method is proposed to achieve efficient cross-scale learning in the domain of authentic distorted image quality assessment.The method undergoes a two-phase training process.In the first phase,cross-scale and cross-color-space image content perception feature is extracted by a self-supervised contrast learning approach.In the second phase,a strategy based on dilated receptive fields and hypernetworks is employed to establish a cyclic feature fusion,which circularly interacts and integrates multi-level feature information with cross-scale information to obtain image quality features closer to human perception.On the basis of the validation on the publicly available authentic distorted image databases,the experimental results demonstrate that the proposed algorithm has achieved superior performance in the quality assessment of authentic distorted images.The experimental results show that the proposed algorithm can realize more efficient cross-scale learning,which provides a good foundation for the application of multi-scale deep network of image processing.