Video Quality Improvement Using Local Channel Encoder and Mixed Predictor by Wavelet, Neural Network and Genetic Algorithm

In recent years, video-based applications have increased. Therefore, researchers are trying to make video coding techniques more effective and efficient. So, several methods are proposed to improve the quality of video against the channel error. The aim of this article is increasing video quality at the receiver. Basis of the proposed method is as follows; in a fixed rate, channel coding rate increases and using it to increase channel error robustness. Channel encoder rate increases with increasing compression rate and decreasing source data. Since the motion estimator block is unable to minimize the variance of the information frames correctly; in this paper, the secondary estimator is proposed which is applied to the information and it causes to increase source compression rate. So the combination of wavelet and neural network with genetic algorithm are used in this secondary estimator. The secondary estimator significantly reduces the information frames variance. Thus, fewer bits are needed to send information. So in this method, channel encoder rate increases without increasing transmitted data for each frame and video frames could be more robustness against to channel error. To evaluate the proposed method, we have tested different source coding rates with several SNRs and compared the results by other methods