现代电子技术2024，Vol.47Issue(9)：47-52,6.DOI:10.16652/j.issn.1004-373x.2024.09.009
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
Multi-scale and multi-level authentic distorted image quality assessment based on RFB and hyper networks
摘要
Abstract
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.关键词
图像质量评价/无参考/真实失真/跨尺度学习/多特征融合/双分支特征提取Key words
image quality assessment/no-reference/authentic distortion/cross-scale learning/multi-feature fusion/double branch feature extraction分类
电子信息工程引用本文复制引用
周怀博,贾惠珍,王同罕..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[J].现代电子技术,2024,47(9):47-52,6.基金项目
国家自然科学基金项目(62266001) （62266001）
国家自然科学基金项目(62261001) （62261001）
