Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz

Abstract

BackgroundThe existing techniques for simultaneous encryption and compression of images refer lossy compression. Their reconstruction performances did not meet the accuracy of medical images because most of them have not been applicable to three-dimensional (3D) medical image volumes intrinsically represented by tensors.MethodsWe propose a tensor-based algorithm using tensor compressive sensing (TCS) to address these issues. Alternating least squares is further used to optimize the TCS with measurement matrices encrypted by discrete 3D Lorenz.ResultsThe proposed method preserves the intrinsic structure of tensor-based 3D images and achieves a better balance of compression ratio, decryption accuracy, and security. Furthermore, the characteristic of the tensor product can be used as additional keys to make unauthorized decryption harder.ConclusionsNumerical simulation results verify the validity and the reliability of this scheme.

Joint image compression and encryption based on sparse Bayesian learning and bit-level 3D Arnold cat maps

“…Chai et al used wavelet transformation, zigzag operations, CS and chaos-based measurement matrices to compress and encrypt images [33]. Other JICE approaches with CS and chaotic systems are associated with the Fibonacci-Lucas transform [34], the optimized tensor CS and 3D Lorenz system [35], the 2D CS with a discrete fractional random transform [36], and so on [37–40].…”