Overview of Image Quality Assessment Method Based on Deep Learning

被引：5

作者：

Cao, Yudong ^{[1
]}

Liu, Haiyan ^{[1
]}

Jia, Xu ^{[1
]}

Li, Xiaohui ^{[1
]}

机构：

[1] College of Electronics and Information Engineering, Liaoning University of Technology, Liaoning, Jinzhou,121001, China

来源：

Computer Engineering and Applications | 2024年 / 57卷 / 23期

关键词：

Convolution - Convolutional neural networks - Deep learning - Image enhancement - Image quality - Learning algorithms - Quality control;

D O I：

10.3778/j.issn.1002-8331.2106-0228

中图分类号：

学科分类号：

摘要：

Image quality evaluation is a measurement of the visual quality of an image or video. The researches on image quality evaluation algorithms in the past 10 years are reviewed. First, the measurement indicators of image quality evaluation algorithm and image quality evaluation datasets are introduced. Then, the different classification of image quality evaluation methods are analyzed, and image quality evaluation algorithms with deep learning technology are focused on, basic model of which is deep convolutional network, deep generative adversarial network and transformer. The performance of algorithms with deep learning is often higher than that of traditional image quality assessment algorithms. Subsequently, the principle of image quality assessment with deep learning is described in detail. A specific no-reference image quality evaluation algorithm based on deep generative adversarial network is introduced, which improves the reliability of simulated reference images through enhanced confrontation learning. Deep learning technology requires massive data support. Data enhancement methods are elaborated to improve the performance of the model. Finally, the future research trend of digital image quality evaluation is summarized. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：27 / 36