Higher-order singular value decomposition-based discrete fractional random transform for simultaneous compression and encryption of video images

Qingzhu Wang; Xiaoming Chen; Yihai Zhu

doi:10.1117/1.JEI.26.5.053014

20 September 2017 Higher-order singular value decomposition-based discrete fractional random transform for simultaneous compression and encryption of video images

Qingzhu Wang, Xiaoming Chen, Yihai Zhu

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 5, 053014 (September 2017). https://doi.org/10.1117/1.JEI.26.5.053014

Abstract

Existing image compression and encryption methods have several shortcomings: they have low reconstruction accuracy and are unsuitable for three-dimensional (3D) images. To overcome these limitations, this paper proposes a tensor-based approach adopting tensor compressive sensing and tensor discrete fractional random transform (TDFRT). The source video images are measured by three key-controlled sensing matrices. Subsequently, the resulting tensor image is further encrypted using 3D cat map and the proposed TDFRT, which is based on higher-order singular value decomposition. A multiway projection algorithm is designed to reconstruct the video images. The proposed algorithm can greatly reduce the data volume and improve the efficiency of the data transmission and key distribution. The simulation results validate the good compression performance, efficiency, and security of the proposed algorithm.

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Qingzhu Wang, Xiaoming Chen, and Yihai Zhu "Higher-order singular value decomposition-based discrete fractional random transform for simultaneous compression and encryption of video images," Journal of Electronic Imaging 26(5), 053014 (20 September 2017). https://doi.org/10.1117/1.JEI.26.5.053014

Received: 29 March 2017; Accepted: 30 August 2017; Published: 20 September 2017

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