A flexible QoE framework for video streaming services

Álvarez, Ana; Cabrero, Sergio; Pañeda, Xabiel G.; Garcı́a, Roberto; Melendi, David; Orea, Rafael

doi:10.1109/glocomw.2011.6162377

Cited by 9 publications

(14 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SSIM and MS-SSIM index ranges from 0 (no structural similarity) and 1 (two identical sets of data) [11]. PSNR, PSNR-HVS, and PSNR-HVS-M range between 20 dB (lower quality) and 60 dB (good quality) [53]. PESQ, ViSQOL, and POLQA are perceptual models for audio quality in which their scores range from 1 (bad) to 5 (excellent) [18,20,54].…”

Section: Objective Analysismentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Objective Analysismentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…PSNR is one of the most commonly used objective measures but it often has been critiqued for providing results that are not fully consistent with subjective quality assessments [1]. However, its ease of implementation and interpretation make it valuable [24].…”

Section: Related Studiesmentioning

confidence: 99%

“…Their experimental results were close to a subjective QoE test method. A video QoE evaluation method that synchronized the reference and distorted videos to avoid an erroneous match was reported and was validated with a subjective video database [24]. Video streaming services in radio-over-fibre (RoF) networks were studied [27].…”

Section: Related Studiesmentioning

confidence: 99%

Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System

Salama¹,

Saatchi²

2019

Technologies

View full text Add to dashboard Cite

Video transmission over wireless computer networks is increasingly popular as new applications emerge and wireless networks become more widespread and reliable. An ability to quantify the quality of a video transmitted using a wireless computer network is important for determining network performance and its improvement. The process requires analysing the images making up the video from the point of view of noise and associated distortion as well as traffic parameters represented by packet delay, jitter and loss. In this study a modular fuzzy logic based system was developed to quantify the quality of video transmission over a wireless computer network. Peak signal to noise ratio, structural similarity index and image difference were used to represent the user's quality of experience (QoE) while packet delay, jitter and percentage packet loss ratio were used to represent traffic related quality of service (QoS). An overall measure of the video quality was obtained by combining QoE and QoS values. Systematic sampling was used to reduce the number of images processed and a novel scheme was devised whereby the images were partitioned to more sensitively localize distortions. To further validate the developed system, a subjective test involving 25 participants graded the quality of the received video. The image partitioning significantly improved the video quality evaluation. The subjective test results correlated with the developed fuzzy logic approach. The video quality assessment developed in this study was compared against a method that uses spatial efficient entropic differencing and consistent results were observed. The study indicated that the developed fuzzy logic approaches could accurately determine the quality of a wirelessly transmitted video.

show abstract

“…Furthermore, other frameworks had been presented to enhance QoE concept such as the flexible QoE framework for video streaming services. The new service evaluations are QoE metrics to achieve high-performance network quality [10]. In the respect, there are various types of performance that can be considered such as Network Performance, Network Performance Overall, QoE, and end-to-end QoS.…”

mentioning

confidence: 99%

Proposed Approach for Enhancement of QoE to Support e-Society

Al-Janabi

Ghareeb²

2016

juhd

View full text Add to dashboard Cite

In recent years, multimedia services and social media applications have become extremely popular as they have been accessed by laptop computers and mobile devices. These can be considered as basic communications functionalities required for building e-society. The heterogeneity of hardware capabilities and network environments result in big challenges for service providers in always providing users with a satisfying experience. The meaning of Quality of Experience (QoE) reflects the degree of a user's subjective satisfaction, which can be different from the Quality of Service (QoS) concept as QoS is solely related to objective system performance metrics, such as the bandwidth, delay, and loss rate. In this work, we propose a general approach for improving QoE through QoS such as to support various e-society applications and services. Various models and parameters related to this issue are reviewed. The proposal represents a framework that considers different aspects such as classification, marking, routing, and queuing to achieve better controlling of relative QoS parameters.

show abstract

A flexible QoE framework for video streaming services

Cited by 9 publications

References 8 publications

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

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

Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System

Proposed Approach for Enhancement of QoE to Support e-Society

Contact Info

Product

Resources

About