摘要
随着深度学习的不断发展与图像数据的爆炸式增长,如何使用深度学习来获得更高压缩比和更高质量的图像逐渐成为热点研究问题之一。通过对近几年相关文献的分析与整理,将基于深度学习的图像压缩方法按照卷积神经网络、循环神经网络、生成对抗网络进行总结与分析,对不同种方法分别列举了具有代表性的实例,并对基于深度学习的图像压缩算法的常用训练数据集、评价指标进行了介绍,根据深度学习在图像压缩领域中的优势对其未来的发展趋势进行了总结与讨论。
With the continuous development of deep learning and the explosive growth of image data,how to use deep learning to obtain higher compression ratio and higher quality images has gradually become one of the hot research issues.Through the analysis of the related literatures in recent years,the image compression method based on the deep learning is summarized and analyzed according to the Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Generative Adversarial Network(GAN).This paper enumerates the typical examples,and the image compression algorithm based on depth study of the training data set,commonly used evaluation indexes are introduced,according to the deep learning advantages in the field of image compression for its future development trend are summarized and discussed.
作者
于恒
梅红岩
许晓明
贾慧萍
YU Heng;MEI Hongyan;XU Xiaoming;JIA Huiping(School of Electronics and Information Engineering,Liaoning University of Technology,Jinzhou,Liaoning 121001,China)
出处
《计算机工程与应用》
CSCD
北大核心
2020年第15期15-23,共9页
Computer Engineering and Applications
基金
辽宁省自然科学基金(No.20180550886,No.201602372)。
关键词
深度学习
图像压缩
卷积神经网络
循环神经网络
生成对抗网络
deep learning
image compression
Convolutional Neural Network(CNN)
Recurrent Neural Network(RNN)
Generative Adversarial Network(GAN)
