The increasing use of video compression standards in broadcasting television systems has required, in recent years, the development of video quality measurements that take into account artifacts specifically caused by digital compression techniques. In this paper we present a methodology for the objective quality assessment of MPEG video streams by using circular backpropagation feedforward neural networks. Mapping neural networks can render nonlinear relationships between objective features and subjective judgments, thus avoiding any simplifying assumption on the complexity of the model. The neural network processes an instantaneous set of input values, and yields an associated estimate of perceived quality. Therefore, the neural-network approach turns objective quality assessment into adaptive modeling of subjective perception. The objective features used for the estimate are chosen according to the assessed relevance to perceived quality and are continuously extracted in real time from compressed video streams. The overall system mimics perception but does not require any analytical model of the underlying physical phenomenon. The capability to process compressed video streams represents an important advantage over existing approaches, like avoiding the streamdecoding process greatly enhances real-time performance. Experimental results confirm that the system provides satisfactory, continuous-time approximations for actual scoring curves concerning real test videos.