摘要
为了是提高GAN模型生成的图像质量的预测精度,以更好地符合人类对图像质量的主观评价,本研究介绍了一种基于知识蒸馏的半监督图像质量评价(IQA)方法。该方法利用CNN和ViT模型的结合,充分捕获全局和局部信息,学习高质量图像与失真图像之间特征的分布差异,并通过知识蒸馏传输高级特征信息。模型通过前向传播,得到图像质量评价分数。为了提高输入特征的多样性和模型的处理速度,本研究还采用了级联分组注意(CGA)机制对输入特征处理。通过在多个公共数据集上的实验表明,本研究方法的评价结果优于现有的评价方法,总体效果理想,相对鲁棒性相对优异,可以获得更加符合人类视觉效果的IQA结果。
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.
作者
闫嘉阔
司占军
YAN Jia-kuo;SI Zhan-jun(College of Light Industry Science and Engineering,Tianjin University of Science and Technology,Tianjin 300457,China;College of Artificial Intelligence,Tianjin University of Science and Technology,Tianjin 300457,China)
出处
《印刷与数字媒体技术研究》
CAS
北大核心
2024年第1期51-59,共9页
Printing and Digital Media Technology Study
关键词
知识蒸馏
GAN模型
图像质量评价
级联分组注意力
Knowledge distillation
GAN model
Image quality assessment
Cascaded Group Attention
