Comparison Of Image Compression Analysis Using Deep Autoencoder And Deep CNN Approach

P. S. Hitha, G. Ragesh, Dr. Anish R
{"title":"Comparison Of Image Compression Analysis Using Deep Autoencoder And Deep CNN Approach","authors":"P. S. Hitha, G. Ragesh, Dr. Anish R","doi":"10.1109/ACCESS51619.2021.9563328","DOIUrl":null,"url":null,"abstract":"Image compression is a fundamental technique in digital image processing used to decrease the space used for storage of digital images and videos, which will help to increase the storage space and for efficient transmission. Nowadays many deep learning techniques have produced promising results on image compression field. However, traditional compression techniques have introduced many compression artifacts problem. To solve this problem we have compared two deep learning approaches for image compression. One method is based on Deep Autoencoder technique and other is based on deep convolutional neural network (deep CNN) approach. Autoencoder structure is a popular choice to do end-to-end compression and deep CNN is the most popular neural network model for the application of any basic deep learning technique. The performance of two methods are compared based on Peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). Based on the performance evaluation methods result, it is evident that deep Autoencoder technique is more advantageous than deep CNN technique.","PeriodicalId":409648,"journal":{"name":"2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACCESS51619.2021.9563328","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Image compression is a fundamental technique in digital image processing used to decrease the space used for storage of digital images and videos, which will help to increase the storage space and for efficient transmission. Nowadays many deep learning techniques have produced promising results on image compression field. However, traditional compression techniques have introduced many compression artifacts problem. To solve this problem we have compared two deep learning approaches for image compression. One method is based on Deep Autoencoder technique and other is based on deep convolutional neural network (deep CNN) approach. Autoencoder structure is a popular choice to do end-to-end compression and deep CNN is the most popular neural network model for the application of any basic deep learning technique. The performance of two methods are compared based on Peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). Based on the performance evaluation methods result, it is evident that deep Autoencoder technique is more advantageous than deep CNN technique.
基于深度自编码器和深度CNN方法的图像压缩分析比较
图像压缩是数字图像处理中的一项基本技术,用于减少用于存储数字图像和视频的空间,从而有助于增加存储空间和提高传输效率。目前,许多深度学习技术在图像压缩领域取得了可喜的成果。然而,传统的压缩技术引入了许多压缩伪影问题。为了解决这个问题,我们比较了两种用于图像压缩的深度学习方法。一种方法是基于深度自编码器技术,另一种方法是基于深度卷积神经网络(Deep CNN)方法。自编码器结构是进行端到端压缩的流行选择,深度CNN是任何基础深度学习技术应用中最流行的神经网络模型。基于峰值信噪比(PSNR)和均方根误差(RMSE)对两种方法的性能进行了比较。从性能评估方法的结果可以看出,深度自编码器技术比深度CNN技术更有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信