Assessment of QoE for Video and Audio in WebRTC Applications Using Full-Reference Models
| Autor: | Andrew Hines, Francisco Gortázar, Boni García, Micael Gallego |
|---|---|
| Přispěvatelé: | European Commission, Ministerio de Ciencia e Innovación (España) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Video quality
video quality Computer Networks and Communications Computer science Structural similarity Mean opinion score Real-time computing lcsh:TK7800-8360 Machine learning & statistics 02 engineering and technology WebRTC POLQA full-reference 0202 electrical engineering electronic engineering information engineering Quality of experience Electrical and Electronic Engineering Sound quality Jitter audio quality Telecomunicaciones lcsh:Electronics Audio quality 020206 networking & telecommunications webrtc Hardware and Architecture Control and Systems Engineering Signal Processing 020201 artificial intelligence & image processing QoE qoe Full-reference PESQ |
| Zdroj: | Electronics, Vol 9, Iss 3, p 462 (2020) e-Archivo: Repositorio Institucional de la Universidad Carlos III de Madrid Universidad Carlos III de Madrid (UC3M) e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname Electronics Volume 9 Issue 3 |
| Popis: | 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. 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. |
| Databáze: | OpenAIRE |
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