On the Impact of Discrete Atomic Compression on Image Classification by Convolutional Neural Networks

Author:

Makarichev Viktor1ORCID,Lukin Vladimir1ORCID,Brysina Iryna2

Affiliation:

1. Department of Information-Communication Technologies, National Aerospace University “KhAI”, 61070 Kharkiv, Ukraine

2. Department of Higher Mathematics and System Analysis, National Aerospace University “KhAI”, 61070 Kharkiv, Ukraine

Abstract

Digital images play a particular role in a wide range of systems. Image processing, storing and transferring via networks require a lot of memory, time and traffic. Also, appropriate protection is required in the case of confidential data. Discrete atomic compression (DAC) is an approach providing image compression and encryption simultaneously. It has two processing modes: lossless and lossy. The latter one ensures a higher compression ratio in combination with inevitable quality loss that may affect decompressed image analysis, in particular, classification. In this paper, we explore the impact of distortions produced by DAC on performance of several state-of-the-art classifiers based on convolutional neural networks (CNNs). The classic, block-splitting and chroma subsampling modes of DAC are considered. It is shown that each of them produces a quite small effect on MobileNetV2, VGG16, VGG19, ResNet50, NASNetMobile and NASNetLarge models. This research shows that, using the DAC approach, memory expenses can be reduced without significant degradation of performance of the aforementioned CNN-based classifiers.

Publisher

MDPI AG

Reference50 articles.

1. Mourtzis, D., Angelopoulos, J., and Panopoulos, N. (2022). A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0. Energies, 15.

2. A comprehensive study on cur-rent and future trends towards the characteristics and enablers of industry 4.0;Karnik;J. Ind. Inf. Integr.,2022

3. Social Presence: Conceptualization and Measurement;Kreijns;Educ. Psychol. Rev.,2022

4. An ethno-methodological analysis of on-line communications. A crisis experiment in chats;Bataeva;Sotsiologicheskie Issled.,2011

5. The Growing Trend of Digital Economy: A Review Article;Limna;Int. J. Comput. Sci. Res.,2022

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3