Title :
Lossless chaos-based crypto-compression scheme for image protection
Author :
Masmoudi, Ahmed ; Puech, William
Author_Institution :
Dept. of Math. & Comput. Sci., Univ. of Sfax, Sfax, Tunisia
Abstract :
In this study, the authors proposed a new scheme which performs both lossless compression and encryption of images. Lossless compression is done by arithmetic coding (AC) while encryption is based on a chaos-based pseudorandom bit generator. Hence, they proposed to incorporate recent results of chaos theory into AC in order to shuffle the cumulative frequency vector of input symbols chaotically to make AC secure and the decoding process completely key-dependent. Many other techniques based on varying the statistical model used by AC have been proposed in literature, however, these techniques suffer from losses in compression efficiency that result from changes in entropy model statistics and are weak against known attacks. The proposed compression-encryption techniques were developed and discussed. The numerical simulation analysis indicates that the proposed scheme is highly satisfactory for image encryption without any AC compression efficiency loss. In addition, it can be incorporated into any image compression standard or algorithm employing AC as entropy coding stage, including static, adaptive and context-based adaptive models, and at any level, including bit, pixel and predictive error pixel levels.
Keywords :
chaos; chaotic communication; cryptography; data compression; decoding; entropy codes; image coding; image resolution; statistical analysis; vectors; adaptive model; arithmetic coding; bit level; chaos theory; chaos-based pseudorandom bit generator; compression efficiency; compression-encryption techniques; context-based adaptive model; cumulative frequency vector; decoding process; entropy coding stage; entropy model statistics; image compression standard; image encryption; image protection; input symbols; lossless chaos-based crypto-compression scheme; numerical simulation analysis; pixel level; predictive error pixel level; static model; statistical model;
Journal_Title :
Image Processing, IET
DOI :
10.1049/iet-ipr.2013.0598