The Experts below are selected from a list of 31842 Experts worldwide ranked by ideXlab platform

M Brindha - One of the best experts on this subject based on the ideXlab platform.

  • a chaos based image encryption and lossless Compression Algorithm using hash table and chinese remainder theorem
    Applied Soft Computing, 2016
    Co-Authors: M Brindha, Ammasai N Gounden
    Abstract:

    No useful information is leaked out as full encryption is done.A very larger key space of 10195 is achieved.Complex diffusion matrix provides strong sensitivity.A lossless Compression ratio of 5:1 is achieved. A chaos based image encryption and lossless Compression Algorithm using hash table and Chinese Remainder Theorem is proposed. Initially, the Henon map is used to generate the scrambled blocks of the input image. The scrambled block undergoes a fixed number of iterations based on the plain image using Arnold cat map. Since hyper chaos system has complex dynamical characteristics than chaos, the confused image is further permuted using the index sequence generated by the hyper chaos along with hash table structure. The permuted image is divided into blocks and the diffusion is carried out either by using Lorenz equations or by using another complex matrix generated from the plain image appropriately. Along with diffusion, Compression is also carried out by Chinese Remainder Theorem for each block. This encryption Algorithm has high key space, good NPCR and UACI values and very less correlation among adjacent pixels. Simulation results show the high effectiveness and security features of the proposed Algorithm.

  • A chaos based image encryption and lossless Compression Algorithm using hash table and Chinese Remainder Theorem
    Applied Soft Computing Journal, 2016
    Co-Authors: M Brindha, N. Ammasai Gounden
    Abstract:

    A chaos based image encryption and lossless Compression Algorithm using hash table and Chinese Remainder Theorem is proposed. Initially, the Henon map is used to generate the scrambled blocks of the input image. The scrambled block undergoes a fixed number of iterations based on the plain image using Arnold cat map. Since hyper chaos system has complex dynamical characteristics than chaos, the confused image is further permuted using the index sequence generated by the hyper chaos along with hash table structure. The permuted image is divided into blocks and the diffusion is carried out either by using Lorenz equations or by using another complex matrix generated from the plain image appropriately. Along with diffusion, Compression is also carried out by Chinese Remainder Theorem for each block. This encryption Algorithm has high key space, good NPCR and UACI values and very less correlation among adjacent pixels. Simulation results show the high effectiveness and security features of the proposed Algorithm.

N. Ammasai Gounden - One of the best experts on this subject based on the ideXlab platform.

  • A chaos based image encryption and lossless Compression Algorithm using hash table and Chinese Remainder Theorem
    Applied Soft Computing Journal, 2016
    Co-Authors: M Brindha, N. Ammasai Gounden
    Abstract:

    A chaos based image encryption and lossless Compression Algorithm using hash table and Chinese Remainder Theorem is proposed. Initially, the Henon map is used to generate the scrambled blocks of the input image. The scrambled block undergoes a fixed number of iterations based on the plain image using Arnold cat map. Since hyper chaos system has complex dynamical characteristics than chaos, the confused image is further permuted using the index sequence generated by the hyper chaos along with hash table structure. The permuted image is divided into blocks and the diffusion is carried out either by using Lorenz equations or by using another complex matrix generated from the plain image appropriately. Along with diffusion, Compression is also carried out by Chinese Remainder Theorem for each block. This encryption Algorithm has high key space, good NPCR and UACI values and very less correlation among adjacent pixels. Simulation results show the high effectiveness and security features of the proposed Algorithm.

Ammasai N Gounden - One of the best experts on this subject based on the ideXlab platform.

  • a chaos based image encryption and lossless Compression Algorithm using hash table and chinese remainder theorem
    Applied Soft Computing, 2016
    Co-Authors: M Brindha, Ammasai N Gounden
    Abstract:

    No useful information is leaked out as full encryption is done.A very larger key space of 10195 is achieved.Complex diffusion matrix provides strong sensitivity.A lossless Compression ratio of 5:1 is achieved. A chaos based image encryption and lossless Compression Algorithm using hash table and Chinese Remainder Theorem is proposed. Initially, the Henon map is used to generate the scrambled blocks of the input image. The scrambled block undergoes a fixed number of iterations based on the plain image using Arnold cat map. Since hyper chaos system has complex dynamical characteristics than chaos, the confused image is further permuted using the index sequence generated by the hyper chaos along with hash table structure. The permuted image is divided into blocks and the diffusion is carried out either by using Lorenz equations or by using another complex matrix generated from the plain image appropriately. Along with diffusion, Compression is also carried out by Chinese Remainder Theorem for each block. This encryption Algorithm has high key space, good NPCR and UACI values and very less correlation among adjacent pixels. Simulation results show the high effectiveness and security features of the proposed Algorithm.

Guillermo Sapiro - One of the best experts on this subject based on the ideXlab platform.

  • loco i a low complexity context based lossless image Compression Algorithm
    Data Compression Conference, 1996
    Co-Authors: M J Weinberger, Gadiel Seroussi, Guillermo Sapiro
    Abstract:

    LOCO-I (low complexity lossless Compression for images) is a novel lossless Compression Algorithm for continuous-tone images which combines the simplicity of Huffman coding with the Compression potential of context models, thus "enjoying the best of both worlds." The Algorithm is based on a simple fixed context model, which approaches the capability of the more complex universal context modeling techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with a collection of (context-conditioned) Huffman codes, which is realized with an adaptive, symbol-wise, Golomb-Rice code. LOCO-I attains, in one pass, and without recourse to the higher complexity arithmetic coders, Compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. In fact, LOCO-I is being considered by the ISO committee as a replacement for the current lossless standard in low-complexity applications.

Dj Wheeler - One of the best experts on this subject based on the ideXlab platform.

  • A block-sorting lossless data Compression Algorithm
    Algorithm Data Compression, 1994
    Co-Authors: Michael Burrows, Dj Wheeler
    Abstract:

    We describe a block-sorting, lossless data Compression Algorithm, and our imple- mentation of that Algorithm. We compare the performance of our implementation with widely available data compressors running on the same hardware. The Algorithmworks by applying a reversible transformation to a block of input text. The transformation does not itself compress the data, but reorders it to make it easy to compress with simple Algorithms such as move-to-front coding. OurAlgorithm achieves speed comparable to Algorithmsbased on the techniques of Lempel and Ziv, but obtains Compression close to the best statisticalmodelling techniques. The size of the input block must be large (a few kilobytes) to achieve good Compression.