DocumentCode
1765063
Title
Designing an Efficient Image Encryption-Then-Compression System via Prediction Error Clustering and Random Permutation
Author
Jiantao Zhou ; Xianming Liu ; Au, Oscar C. ; Yuan Yan Tang
Author_Institution
Dept. of Comput. & Inf. Sci., Univ. of Macau, Taipa, China
Volume
9
Issue
1
fYear
2014
fDate
Jan. 2014
Firstpage
39
Lastpage
50
Abstract
In many practical scenarios, image encryption has to be conducted prior to image compression. This has led to the problem of how to design a pair of image encryption and compression algorithms such that compressing the encrypted images can still be efficiently performed. In this paper, we design a highly efficient image encryption-then-compression (ETC) system, where both lossless and lossy compression are considered. The proposed image encryption scheme operated in the prediction error domain is shown to be able to provide a reasonably high level of security. We also demonstrate that an arithmetic coding-based approach can be exploited to efficiently compress the encrypted images. More notably, the proposed compression approach applied to encrypted images is only slightly worse, in terms of compression efficiency, than the state-of-the-art lossless/lossy image coders, which take original, unencrypted images as inputs. In contrast, most of the existing ETC solutions induce significant penalty on the compression efficiency.
Keywords
arithmetic codes; data compression; image coding; pattern clustering; prediction theory; random codes; ETC; arithmetic coding-based approach; image encryption-then-compression system design; lossless compression; lossless image coder; lossy compression; lossy image coder; prediction error clustering; random permutation; security; Bit rate; Decoding; Encryption; Image coding; Image reconstruction; Compression of encrypted image; encrypted domain signal processing;
fLanguage
English
Journal_Title
Information Forensics and Security, IEEE Transactions on
Publisher
ieee
ISSN
1556-6013
Type
jour
DOI
10.1109/TIFS.2013.2291625
Filename
6670767
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