New DCT-based image coding method using random neural network

Image compression is an important area of multimedia investigation and neural network methods have attracted more and more attentions for using in image coding. Recently a random neural network model, which has the solutions with product form in steady state (i.e. the steady state probability distribution of network can always be expressed as the product of the probabilities of the states of each neuron) on some conditions, was brought forward. Among the diverse random neural network models, the feed-forward one is very practicable because its solutions exist and are unique. In this paper, a new learning method for feed-forward random neural network, which can be implemented easier than the learning algorithm of the RNN presented by Gelenbe, was presented. Using the new learning formulas we developed, we designed a new image coding method, which applies the random neural network method in classical DCT-based coding framework. The experimental results show that our new method could gain a lot in PSNR (1 similar to 2dB) compared with standard neural network coding methods. In conclusion, we stated that the DCT-based image compression method using random neural network is an efficient algorithm for image coding.