A comparative study of nonlinear video rate control techniques: neural networks and fuzzy logic

Data rate management of compressed digital video has been treated mainly from the teletraffic control point of view, i.e. by modelling traffic or controlling congestion via network protocols. Relatively less attention has been focused on video rate management in the source coding side. We consider that it is more efficient and less costly to control the video rate at the video source than handling network congestion due to an extremely large quantity of incoming variable bit rate (VBR) video traffic. Thus this paper investigates effective rate control algorithms for video encoders. Considering the non-stationary nature of the video rate originated from scene variations (i.e. the wide band nature of digital video), we adopted and compared the performance of two nonlinear approaches; radial basis function (RBF) estimation using a neural network-based approach and fuzzy logic control as a nonlinear feedback control