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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.
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