A Fuzzy Neural CBR Channel Rate Controller for MPEG2 Encoders

"A fuzzy algorithm is used as control surface for the buffer occupancy of a MPEG2 (Moving Picture Experts Group) video encoder. Based on scene features, a supervised algorithm trains a Radial Basis Function Neural Network (RBFNN). The so trained RBFNN acts as a predictor for the number of bits generated in a frame, so that the predicted buffer occupancy can be determined. The predicted and present buffer occupancies are applied to the fuzzy-generated control surface which yields the encoder quantizer step parameter. We compare the obtained results with the Test Model 5 standard rate control scheme. "