HWCD:一种使用小波进行图像压缩、使用混淆进行加密和使用扩散方案进行解密的混合方法

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2023-01-01 DOI:10.1515/jisys-2022-9056
H. R. Latha, Alagarswamy Ramaprasath
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引用次数: 1

摘要

图像数据在各种实时在线和离线应用中发挥着重要作用。生物医学领域已经采用成像系统来检测、诊断和预防多种疾病和异常。生物医学成像数据信息量巨大,需要巨大的存储空间。此外,目前广泛开发了远程医疗和基于物联网的远程健康监测系统,其中数据从一个地方传输到另一个地方。这种类型的大数据传输消耗更多的带宽。同时,在这种传输过程中,攻击者可以攻击通信通道,获取重要的机密信息。因此,生物医学图像压缩和加密被认为是解决这些问题的解决方案。已经提出了几种技术,但要实现组合模块所需的性能是一项具有挑战性的任务。因此,在这项工作中,开发了一种新的图像压缩和加密组合方法。首先提出了一种基于小波变换的图像压缩方案,然后提出了一种基于混淆和扩散的加密方案。该方法的结果与现有的各种技术进行了比较。实验分析表明,该方法在自相关、直方图、信息熵、PSNR、MSE和SSIM方面都取得了较好的性能。
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HWCD: A hybrid approach for image compression using wavelet, encryption using confusion, and decryption using diffusion scheme
Abstract Image data play important role in various real-time online and offline applications. Biomedical field has adopted the imaging system to detect, diagnose, and prevent several types of diseases and abnormalities. The biomedical imaging data contain huge information which requires huge storage space. Moreover, currently telemedicine and IoT based remote health monitoring systems are widely developed where data is transmitted from one place to another. Transmission of this type of huge data consumes more bandwidth. Along with this, during this transmission, the attackers can attack the communication channel and obtain the important and secret information. Hence, biomedical image compression and encryption are considered the solution to deal with these issues. Several techniques have been presented but achieving desired performance for combined module is a challenging task. Hence, in this work, a novel combined approach for image compression and encryption is developed. First, image compression scheme using wavelet transform is presented and later a cryptography scheme is presented using confusion and diffusion schemes. The outcome of the proposed approach is compared with various existing techniques. The experimental analysis shows that the proposed approach achieves better performance in terms of autocorrelation, histogram, information entropy, PSNR, MSE, and SSIM.
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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