z-logo
open-access-imgOpen Access
Real-time monitoring of video quality in IP networks
Author(s) -
Tao Shu,
John Apostolopoulos,
R. Guérin
Publication year - 2005
Publication title -
scholarly commons (university of pennsylvania)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/1065983.1066013
Subject(s) - computer science , video quality , real time computing , codec , packet loss , metric (unit) , network packet , benchmark (surveying) , rate–distortion optimization , quality (philosophy) , scale (ratio) , computer network , video processing , video tracking , multiview video coding , artificial intelligence , telecommunications , operations management , philosophy , epistemology , physics , geodesy , quantum mechanics , economics , geography
This paper investigates the problem of assessing the quality of video transmitted over IP networks. Our goal is to develop a methodology that is both reasonably accurate and simple enough to support the large-scale deployments that the increasing use of video over IP are likely to demand. For that purpose, we focus on developing an approach that is capable of mapping network statistics, e.g., packet losses, available from simple measurements, to the quality of video sequences reconstructed by receivers. A first step in that direction is a loss-distortion model that accounts for the impact of network losses on video quality, as a function of application-specific parameters such as video codec, loss recovery technique, coded bit rate, packetization, video characteristics, etc. The model, although accurate, is poorly suited to large-scale, on-line monitoring, because of its dependency on parameters that are difficult to estimate in real-time. As a result, we introduce a ldquorelative qualityrdquo metric (rPSNR) that bypasses this problem by measuring video quality against a quality benchmark that the network is expected to provide. The approach offers a lightweight video quality monitoring solution that is suitable for large-scale deployments. We assess its feasibility and accuracy through extensive simulations and experiments.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom