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

Author

Gastaldo, Paolo ; Rovetta, Stefano ; Zunino, Rodolfo

Author_Institution

Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy

Volume

2

fYear

2001

fDate

2001

Firstpage

1432

Abstract

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

Keywords

backpropagation; data compression; feature extraction; feedforward neural nets; real-time systems; video coding; MPEG; adaptive modeling; circular backpropagation; feature extraction; feedforward neural networks; real-time system; video quality assessment; Backpropagation; Digital TV; Digital video broadcasting; Measurement standards; Neural networks; Quality assessment; Streaming media; TV broadcasting; Transform coding; Video compression;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.939572

Filename

939572