Objective Quality Assessment of MPEG-2 Video Streams
by Using CBP Neural Networks
Paolo Gastaldo, Stefano Rovetta, and Rodolfo Zunino

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 1, JANUARY 1999
Pages: 31-47

Abstract —

The increasing use of compression standards in
broadcasting digital TV has raised the need for established criteria
to measure perceived quality. Novel methods must take into
account the specific artifacts introduced by digital compression
techniques. This paper presents a methodology using circular
backpropagation (CBP) neural networks for the objective quality
assessment of motion picture expert group (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. The
overall system mimics perception but does not require an analytical
model of the underlying physical phenomenon. The ability to
process compressed video streams represents a crucial advantage
over existing approaches, as avoiding the decoding process greatly
enhances the system’s real-time performance. Experimental
evidence confirmed the approach validity. The system was tested
on real test videos; they included different contents ranging from
fiction to sport. The neural model provided a satisfactory, continuous-
time approximation for actual scoring curves, which was
validated statistically in terms of confidence analysis. As expected,
videos with slow-varying contents such as fiction featured the best
performances.

Index Terms—

Circular backpropagation (CBP), compressed video,
digital TV, objective quality assessment