VIDEO QUALITY IMPROVEMENT USING LOCAL CHANNEL ENCODER AND MIXED PREDICTOR BY WAVELET, NEURAL NETWORK AND GENETIC ALGORITHM

In recent decades, progress of technology causes that video transmission via communication channels has met high demands. Therefore, many methods have been proposed to improve the quality of video under channel errors. The aim of this paper is to increase PSNR for reconstructed video at receiver. This is achieved by increasing channel encoder rate but in constant transmission rate. Since the motion compensation block is unable to minimum the variance of frame information, accurately, the proposed method is applied after this block. We proposed the secondary predictor by combination of wavelet transform, neural network and Genetic Algorithm (GA). This predictor reduces the variance of frame information considerably, and therefore lower bits are required for transmission. Thus, it is possible to apply the local channel encoder whose rate depends on variance reduction. The proposed method is able to increase channel coding rate without increasing the amount of information for any frame. This method provides more robustness for video frames against channel errors. In order to evaluate the performance of the proposed method, it is tested for different source coding rates and several SNRs for channel and the obtained results are compared with a new method.