A novel image compression–encryption hybrid algorithm based on the analysis sparse representation

Recent advances on the compressive sensing theory were invoked for image compression–encryption based on the synthesis sparse model. In this paper we concentrate on an alternative sparse representation model, i.e., the analysis sparse model, to propose a novel image compression–encryption hybrid alg...

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Vydáno v:Optics communications Ročník 392; s. 223 - 233
Hlavní autoři: Zhang, Ye, Xu, Biao, Zhou, Nanrun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.06.2017
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ISSN:0030-4018, 1873-0310
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Shrnutí:Recent advances on the compressive sensing theory were invoked for image compression–encryption based on the synthesis sparse model. In this paper we concentrate on an alternative sparse representation model, i.e., the analysis sparse model, to propose a novel image compression–encryption hybrid algorithm. The analysis sparse representation of the original image is obtained with an overcomplete fixed dictionary that the order of the dictionary atoms is scrambled, and the sparse representation can be considered as an encrypted version of the image. Moreover, the sparse representation is compressed to reduce its dimension and re-encrypted by the compressive sensing simultaneously. To enhance the security of the algorithm, a pixel-scrambling method is employed to re-encrypt the measurements of the compressive sensing. Various simulation results verify that the proposed image compression–encryption hybrid algorithm could provide a considerable compression performance with a good security. •A novel image compression–encryption hybrid algorithm is proposed with analysis sparse representation.•Using an atom-scrambled dictionary to obtain the sparse representation of the image.•The sparse representation is an encrypted version of the image.•Compressive sensing is utilized to compress and encrypt the sparse representation.•A pixel-scrambling method is introduced to enhance the security of the algorithm.
ISSN:0030-4018
1873-0310
DOI:10.1016/j.optcom.2017.01.061