Simulation of MPEG video traffic using neural networks

A new model for the simulation of MPEG video traffic is presented. The model, implemented on neural networks, is capable of accurately adjusting the autocorrelation and probability distribution functions of a given video traffic. This adjustment is performed by capturing the projected conditioned histogram of the real traffic, so that the neural model will be able to yield a simulated traffic at its output, just as a function of an input white noise. Moreover, using neural networks we benefit from their inherent capacities for working in real time, because of their parallelism, and interpolating unknown functions. These interpolations avoid the need of searching in transition matrices of other histogram-based methods as well as they reduce the amount of stored information. Results are presented for a real MPEG video source.