A linear regression framework for assessing time-varying subjective quality in HTTP streaming

Eswara, Nagabhushan and Reddy, Dendi Sathya Veera and Channappayya, Sumohana and Kumar, Abhinav and Kuchi, Kiran et. al. (2017) A linear regression framework for assessing time-varying subjective quality in HTTP streaming. In: IEEE Global Conference on Signal and Information Processing (GlobalSIP), 14-16 November 2017, Montreal, QC, Canada.

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Abstract

In an HTTP streaming framework, continuous time quality evaluation is necessary to monitor the time-varying subjective quality (TVSQ) of the videos resulting from rate adaptation. In this paper, we present a novel learning framework for TVSQ assessment using linear regression under the Reduced-Reference (RR) and the No-Reference (NR) settings. The proposed framework relies on objective short time quality estimates and past TVSQs for predicting the present TVSQ. Specifically, we rely on spatio-temporal reduced reference en-tropic differencing for RR and on a 3D convolutional neural network for NR quality estimations. While the proposed RR-TVSQ model delivers competitive performance with state-of-the-art methods, the proposed NR-TVSQ model outperforms state-of-the-art algorithms over the LIVE QoE database.

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IITH Creators:

IITH Creators	ORCiD
Channappayya, Sumohana	UNSPECIFIED
Kuchi, Kiran	UNSPECIFIED
Kumar, Abhinav	UNSPECIFIED

Item Type:

Conference or Workshop Item (Paper)

Uncontrolled Keywords:

3D convolution neural network, C3D, DASH, HD, HTTP streaming, no reference, QoE, quality assessment, rate adaptation, reduced reference, time-varying subjective quality

Subjects:

Electrical Engineering

Divisions:

Department of Electrical Engineering

Depositing User:

Team Library

Date Deposited:

10 May 2019 06:07

Last Modified:

10 May 2019 06:07

URI:

http://raiithold.iith.ac.in/id/eprint/5119