A back-propagation neural network based on a hybrid genetic algorithm and particle swarm optimization for image compression

In this paper, an improved approach integrating genetic algorithm and adaptive particle swarm optimization with feed forward neural networks for image compression is proposed. The hybrid genetic algorithm with a novel mutation strategy and particle swarm optimization is used to train the neural network to near global optimum weights and thresholds at first. Then the network is trained with gradient descending learning algorithm to obtain the optimal network parameters. Then, the trained network is applied to the image compression. Results show that at the same compression rate the application of optimized neural network in image compression will achieve better image quality compared with the application of traditional neural network.