A reduced-reference parametric model for audiovisual quality of IPTV services

Toni Mäki; D. Kukolj; D. Dordevic; Martin Varela

doi:10.1109/QoMEX.2013.6603193

A reduced-reference parametric model for audiovisual quality of IPTV services

Toni Mäki, D. Kukolj, D. Dordevic, Martin Varela

VTT Technical Research Centre of Finland

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

14 Citations (Scopus)

Abstract

In this paper we present a parametric model for audiovisual quality estimation in IPTV and similar services. The proposed model takes advantage of signal characteristics calculated at the sender (in particular related to levels of motion in the content), but is purely parametric on the estimation (i.e. it does not require peeking into the bitstream), which makes it suitable for large-scale real-time monitoring applications. In order to obtain the model, we followed the Pseudo-Subjective Quality Assessment (PSQA) methodology, and compared different kinds of statistical estimators, namely Multilayer Perceptrons (MLP) and Random Neural Networks (RNN).

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	Fifth International Workshop on Quality of Multimedia Experience, QoMEX 2013
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	6-11
ISBN (Electronic)	978-1-4799-0738-0
DOIs	https://doi.org/10.1109/QoMEX.2013.6603193
Publication status	Published - 2013
MoE publication type	Not Eligible
Event	5th International Workshop on Quality of Multimedia Experience, QoMEX 2013 - Klagenfurt am Wörthersee, Austria Duration: 5 Jul 2013 → 7 Jul 2013 Conference number: 5

Conference

Conference	5th International Workshop on Quality of Multimedia Experience, QoMEX 2013
Abbreviated title	QoMEX 2013
Country	Austria
City	Klagenfurt am Wörthersee
Period	5/07/13 → 7/07/13

Keywords

audiovisual quality assessment
feature clustering
neural network
video quality assessment

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title = "A reduced-reference parametric model for audiovisual quality of IPTV services",

abstract = "In this paper we present a parametric model for audiovisual quality estimation in IPTV and similar services. The proposed model takes advantage of signal characteristics calculated at the sender (in particular related to levels of motion in the content), but is purely parametric on the estimation (i.e. it does not require peeking into the bitstream), which makes it suitable for large-scale real-time monitoring applications. In order to obtain the model, we followed the Pseudo-Subjective Quality Assessment (PSQA) methodology, and compared different kinds of statistical estimators, namely Multilayer Perceptrons (MLP) and Random Neural Networks (RNN).",

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Mäki, T, Kukolj, D, Dordevic, D & Varela, M 2013, A reduced-reference parametric model for audiovisual quality of IPTV services. in Proceedings: Fifth International Workshop on Quality of Multimedia Experience, QoMEX 2013. IEEE Institute of Electrical and Electronic Engineers, pp. 6-11, 5th International Workshop on Quality of Multimedia Experience, QoMEX 2013, Klagenfurt am Wörthersee, Austria, 5/07/13. https://doi.org/10.1109/QoMEX.2013.6603193

A reduced-reference parametric model for audiovisual quality of IPTV services. / Mäki, Toni; Kukolj, D.; Dordevic, D.; Varela, Martin.

Proceedings: Fifth International Workshop on Quality of Multimedia Experience, QoMEX 2013. IEEE Institute of Electrical and Electronic Engineers, 2013. p. 6-11.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AB - In this paper we present a parametric model for audiovisual quality estimation in IPTV and similar services. The proposed model takes advantage of signal characteristics calculated at the sender (in particular related to levels of motion in the content), but is purely parametric on the estimation (i.e. it does not require peeking into the bitstream), which makes it suitable for large-scale real-time monitoring applications. In order to obtain the model, we followed the Pseudo-Subjective Quality Assessment (PSQA) methodology, and compared different kinds of statistical estimators, namely Multilayer Perceptrons (MLP) and Random Neural Networks (RNN).

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