A novel hybrid image encryption–compression scheme by combining chaos theory and number theory

Compression and encryption are often performed together for image sharing and/or storage. The order in which the two operations are carried out affects the overall efficiency of digital image services. For example, the encrypted data has less or no compressibility. On the other hand, it is challengi...

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Bibliographic Details
Published in:Signal processing. Image communication Vol. 98; p. 116418
Main Authors: Ahmad, Ijaz, Shin, Seokjoo
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.10.2021
Elsevier BV
Subjects:
Algorithms
Chaos theory
Coders
Compressibility
Digital imaging
Encryption
Image compression
Image encryption
Medical image processing
Medical imaging
Number theory
Performance enhancement
Permutations
Security
Substitutes
Medical image processing
Image encryption
Image compression
Chaos theory
Number theory
ISSN:0923-5965, 1879-2677
Online Access:Get full text
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Summary:Compression and encryption are often performed together for image sharing and/or storage. The order in which the two operations are carried out affects the overall efficiency of digital image services. For example, the encrypted data has less or no compressibility. On the other hand, it is challenging to ensure reasonable security without downgrading the compression performance. Therefore, incorporating one requirement into another is an interesting approach. In this study, we propose a novel hybrid image encryption and compression scheme that allows compression in the encryption domain. The encryption is based on Chaos theory and is carried out in two steps, i.e., permutation and substitution. The lossless compression is performed on the shuffled image and then the compressed bitstream is grouped into 8-bit elements for substitution stage. The lossless nature of the proposed method makes it suitable for medical image compression and encryption applications. The experimental results shows that the proposed method achieves the necessary level of security and preserves the compression efficiency of a lossless algorithm. In addition, to improve the performance of the entropy encoder of the compression algorithm, we propose a data-to-symbol mapping method based on number theory to represent adjacent pixel values as a block. With such representation, the compression saving is improved on average from 5.76% to 15.45% for UCID dataset.
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ISSN:0923-5965
1879-2677
DOI:10.1016/j.image.2021.116418