Research On Genetic Algorithm Applying To Block Truncation Coding Image Information Hiding

Genetic Algorithm (Genetic Algorithm, abbreviated as GA) is an imitation of the natural biological process of evolution of random search and optimization algorithm, its advantage lies in simple, robust, strong global optimization and easy to operate, it is being widely used in the function optimization, machine learning, pattern recognition and adaptive control systems and many other fields. However, Because of the standard GA have such disadvantages as premature convergence, low convergence speed and low robustness which generate the large general mean-square error between the original images and the common bitmaps. in order to overcome these shortcomings, genetic algorithm is used to block truncation coding image information hiding to raise the compression ratio, speed up the convergence speed and so on. Based on in-depth analysis, the main contributions in this dissertation are as follows:The recent status of genetic algorithms is described. The specific strategies of genetic algorithm elements are researched. the scheme of genetic algorithm applying to block truncation coding for image information hiding is used. The scheme includes: The theory for genetic algorithm is mainly based on the individual fitness value and it is found that the selection of the fitness function directly affects the search performance of genetic algorithms. Moreover, proposing fitness function which based on human vision and analysis the detail of the simulation results which showed fast convergence speed and good search capabilities based on human vision fitness function. Proposed the method of copying the three color bitmaps to initialize the original population and also proposed a uniform ranking selection algorithm to promote the evolution of populations. By using the function of sigmoid which constructs the function of neuron activation in neural network activation, we find that it shows a good balance between linear and non-linear. According to individual fitness, the crossover rate, mutation rate are adaptive non-linear adjustment between average fitness and maximum fitness, therefore, an effective way has put forward named adaptive crossover and adaptive mutation to prevent stagnation, break away from the local convergence and improve the algorithm robustness. To improve the local search ability and optimize the population to get the common bitmap, adding evolution reverse operation is presented when genetic algorithm run into the late stage.This paper focuses on the simulation by using MATLAB, and analyses the experimental data and discusses the experimental errors, Although this is only a theory discussion about the use of genetic algorithm combined with the block truncation coding applying to image information hiding to improve the compression ratio, it has important significance for actual information hiding theory.