A parallel block-based encryption schema for digital images using reversible cellular automata

AbstractWe propose a novel images encryption schema based on reversible one-dimensional cellular automata. Contrasting to the sequential operating mode of several existing approaches, the proposed one is fully parallelizable since the encryption/decryption tasks can be executed using multiple processes running independently for the same single image. The parallelization is made possible by defining a new RCA-based construction of an extended pseudorandom permutation that takes a nonce as a supplementary parameter. The defined PRP exploit the chaotic behavior and the high initial condition's sensitivity of the RCAs to ensure perfect cryptographic security properties. Results of various experiments and analysis show that high security and execution performances can be achieved using the approach, and furthermore, it provides the ability to perform a selective area decryption since any part of the ciphered-image can be deciphered independently from others, which is very useful for real time applications

Resolution Scalable Image Coding with Reversible Cellular Automata

In a resolution scalable image coding algorithm, a multiresolution representation of the data is often obtained using a linear filter bank. Reversible cellular automata have been recently proposed as simpler, nonlinear filter banks that produce a similar representation. The original image is decomposed into four subbands, such that one of them retains most of the features of the original image at a reduced scale. In this paper, we discuss the utilization of reversible cellular automata and arithmetic coding for scalable compression of binary and grayscale images. In the binary case, the proposed algorithm that uses simple local rules compares well with the JBIG compression standard, in particular for images where the foreground is made of a simple connected region. For complex images, more efficient local rules based upon the lifting principle have been designed. They provide compression performances very close to or even better than JBIG, depending upon the image characteristics. In the grayscale case, and in particular for smooth images such as depth maps, the proposed algorithm outperforms both the JBIG and the JPEG2000 standards under most coding conditions

Resolution Scalable Image Coding with Reversible Cellular Automata

Abstract-In a resolution scalable image coding algorithm, a multiresolution representation of the data is often obtained using a linear filter bank. Reversible cellular automata have been recently proposed as simpler, non-linear filter banks that produce a similar representation. The original image is decomposed into four subbands, such that one of them retains most of the features of the original image at a reduced scale. In this paper, we discuss the utilization of reversible cellular automata and arithmetic coding for scalable compression of binary and grayscale images. In the binary case, the proposed algorithm that uses simple local rules compares well with the JBIG compression standard, in particular for images where the foreground is made of a simple connected region. For complex images, more efficient local rules based on the lifting principle have been designed. They provide compression performances very close to or even better than JBIG, depending on the image characteristics. In the grayscale case, and in particular for smooth images such as depth maps, the proposed algorithm outperforms both the JBIG and the JPEG2000 standards under most coding conditions