Objective assessment of MPEG-video quality: a neural-network approach

The increasing use of compression standards in broadcasting digital TV has raised the need for established criteria to measure perceived quality. This paper presents a methodology using circular backpropagation (CBP) neural networks for the objective quality assessment of MPEG video streams. Objective features are continuously extracted from compressed video streams on a frame-by-frame basis; they feed the CBP network estimating the corresponding perceived quality. The resulting adaptive modeling of subjective perception supports a real-time system for monitoring displayed video quality