RT Journal Article
T1 Assessment of QoE for video and audio in WebRTC applications using full-reference models
A1 García Gutiérrez, Boni
A1 Gortázar, Francisco
A1 Gallego, Micael
A1 Hines, Andrew
AB 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.
PB MDPI
SN 2079-9292
YR 2020
FD 2020-03-10
LK https://hdl.handle.net/10016/32355
UL https://hdl.handle.net/10016/32355
LA eng
NO This work has been supported by the European Commission under project ElasTest (H2020-ICT-10-2016, GA-731535); by the Regional Government of Madrid (CM) under project EDGEDATA-CM (P2018/TCS-4499) cofunded by FSE and FEDER; by the Spanish Government under projects LERNIM (RTC-2016-4674-7) and BugBirth (RTI2018-101963-B-I00) cofunded by the Ministry of Economy and Competitiveness, FEDER, and AEI; and by Science Foundation Ireland (SFI) cofunded under the European Regional Development Fund under grant number 12/RC/2289_P2 and grant number SFI/12/RC/2077.
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RD 1 sept. 2024