Assessment of QoE for Video and Audio in WebRTC Applications Using Full-Reference Models

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Title: Assessment of QoE for Video and Audio in WebRTC Applications Using Full-Reference Models
Authors: García, BoniGortázar, FranciscoGallego, MicaelHines, Andrew
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Date: 10-Mar-2020
Online since: 2020-12-09T16:45:27Z
Abstract: WebRTC is a set of standard technologies that allows exchanging video and audio in real time on the Web. As with other media-related applications, the user-perceived audiovisual quality can be estimated using Quality of Experience (QoE) measurements. This paper analyses the behavior of different objective Full-Reference (FR) models for video and audio in WebRTC applications. FR models calculate the video and audio quality by comparing some original media reference with the degraded signal. To compute these models, we have created an open-source benchmark in which different types of reference media inputs are sent browser to browser while simulating different kinds of network conditions in terms of packet loss and jitter. Our benchmark provides recording capabilities of the impairment WebRTC streams. Then, we use different existing FR metrics for video (VMAF, VIFp, SSIM, MS-SSIM, PSNR, PSNR-HVS, and PSNR-HVS-M) and audio (PESQ, ViSQOL, and POLQA) recordings together with their references. Moreover, we use the same recordings to carry out a subjective analysis in which real users rate the video and audio quality using a Mean Opinion Score (MOS). Finally, we calculate the correlations between the objective and subjective results to find the objective models that better correspond with the subjective outcome, which is considered the ground truth QoE. We find that some of the studied objective models, such as VMAF, VIFp, and POLQA, show a strong correlation with the subjective results in packet loss scenarios.
Funding Details: European Commission
European Commission - European Regional Development Fund
Science Foundation Ireland
Funding Details: Insight Research Centre
Regional Government of Madrid
Spanish Government
Ministry of Economy and Competitiveness
Type of material: Journal Article
Publisher: MDPI
Journal: Electronics
Volume: 9
Issue: 3
Copyright (published version): 2020 the Authors
Keywords: Machine learning & statisticsQoEWebRTCVideo qualityAudio qualityFull-reference
DOI: 10.3390/electronics9030462
Language: en
Status of Item: Peer reviewed
This item is made available under a Creative Commons License:
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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