A lossless image compression and encryption algorithm combining JPEG-LS, neural network and hyperchaotic system

In this paper, a lossless image compression and encryption algorithm combining JPEG-LS, neural networks and hyperchaotic mapping is proposed to protect the privacy of digital images and reduce data storage space. Firstly, we design a new 2-Dimensional Logistic-Like Hyperchaotic Map (2DLLHM), which has more complex dynamics than some existing known chaotic systems, and can be used to build a good pseudorandom sequence generator. Secondly, to compress images efficiently, we design a new pixel predictor by combining the MED (Median Edge Detector) of JPEG-LS with MLP (Multilayer Perceptron). This predictor is called MMP. The MMP can effectively improve the prediction effect of edge texture area. On this basis, a threshold segmentation method is proposed. The method combined with MMP, run-length coding and Huffman coding can further improve the image compression ratio. Finally, to avoid some of the existing weak encryption designs, we construct a multi-round nonlinear diffusion structure with more excellent diffusion performance. Experiments show that the algorithm achieves a good compression ratio and can resist brute force attacks, statistical attacks, chosen-plaintext attacks and chosen-ciphertext attacks.