In this study, we propose a numerical implementation (using a GPU) of an optimized multiple image compression and encryption technique. We first introduce the double optimization procedure for spectrally multiplexing multiple images. This technique is adapted, for a numerical implementation, from a recently proposed optical setup implementing the Fourier transform (FT)¹. The new analysis technique is a combination of a spectral fusion based on the properties of FT, a specific spectral filtering, and a quantization of the remaining encoded frequencies using an optimal number of bits. The spectral plane (containing the information to send and/or to store) is decomposed in several independent areas which are assigned according a specific way. In addition, each spectrum is shifted in order to minimize their overlap. The dual purpose of these operations is to optimize the spectral plane allowing us to keep the low- and high-frequency information (compression) and to introduce an additional noise for reconstructing the images (encryption). Our results show that not only can the control of the spectral plane enhance the number of spectra to be merged, but also that a compromise between the compression rate and the quality of the reconstructed images can be tuned. Spectrally multiplexing multiple images defines a first level of encryption. A second level of encryption based on a real key image is used to reinforce encryption. Additionally, we are concerned with optimizing the compression rate by adapting the size of the spectral block to each target image and decreasing the number of bits required to encode each block. This size adaptation is realized by means of the root-mean-square (RMS) time-frequency criterion². We have found that this size adaptation provides a good trade-off between bandwidth of spectral plane and number of reconstructed output images³. Secondly, the encryption rate is improved by using a real biometric key and randomly changing the rotation angle of each block before spectral fusion. A numerical implementation of this method using two numerical devices (CPU and GPU) is presented⁴.

Date Published: 20 April 2015
PDF: 5 pages
Proc. SPIE 9477, Optical Pattern Recognition XXVI, 94770M (20 April 2015); doi: 10.1117/12.2178523

Published in SPIE Proceedings Vol. 9477:
Optical Pattern Recognition XXVI
David Casasent; Mohammad S. Alam, Editor(s)