RT Journal Article
T1 Practical evaluation of VMAF perceptual video quality for WebRTC applications
A1 García Gutiérrez, Boni
A1 López-Fernández, Luis
A1 Gortázar, Francisco
A1 Gallego, Micael
AB WebRTC is the umbrella term for several emergent technologies aimed to exchange real-time media in the Web. Like other media-related services, the perceived quality of WebRTC communication can be measured using Quality of Experience (QoE) indicators. QoE assessment methods can be classified as subjective (users' evaluation scores) or objective (models computed as a function of different parameters). In this paper, we focus on VMAF (Video Multi-method Assessment Fusion), which is an emergent full-reference objective video quality assessment model developed by Netflix. VMAF is typically used to assess video streaming services. This paper evaluates the use of VMAF in a different type of application: WebRTC. To that aim, we present a practical use case built on the top of well-known open source technologies, such as JUnit, Selenium, Docker, and FFmpeg. In addition to VMAF, we also calculate other objective QoE video metrics such as Visual Information Fidelity in the pixel domain (VIFp), Structural Similarity (SSIM), or Peak Signal-to-Noise Ratio (PSNR) applied to a WebRTC communication in different network conditions in terms of packet loss. Finally, we compare these objective results with a subjective evaluation using a Mean Opinion Score (MOS) scale to the same WebRTC streams. As a result, we found a strong correlation of the subjective video quality perceived in WebRTC video calls with the objective results computed with VMAF and VIFp in comparison with SSIM and PSNR and their variants.
PB MDPI
SN 2079-9292
YR 2019
FD 2019-08
LK https://hdl.handle.net/10016/32367
UL https://hdl.handle.net/10016/32367
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.
DS e-Archivo
RD 27 jul. 2024