Performance comparison of video quality metrics

Kotevski, Zoran; Mitrevski, Pece

doi:10.1117/12.853442

Cited by 2 publications

(1 citation statement)

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“…A higher PSNR indicates better quality. SSIM is a more complex quantitative metric that considers perceptual quality [25]. Its value lies between zero and one, the latter value implying that the two frames are the same.…”

Section: Measuring Quantitative Performancementioning

confidence: 99%

Comparison of popular video conferencing apps using client-side measurements on different backhaul networks

Kumar

Nagpal

Naik

et al. 2022

Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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Video conferencing platforms have been appropriated during the COVID-19 pandemic for different purposes, including classroom teaching. However, the platforms are not designed for many of these objectives. When users, like educationists, select a platform, it is unclear which platform will perform better given the same network and hardware resources to meet the required Quality of Experience (QoE). Similarly, when developers design a new video conferencing platform, they do not have clear guidelines for making design choices given the QoE requirements.In this paper, we provide a set of networks and systems measurements, and quantitative user studies to measure the performance of video conferencing apps in terms of both, Quality of Service (QoS) and QoE. Using those metrics, we measure the performance of Google Meet, Microsoft Teams, and Zoom, which are three popular platforms in education and business. We find a substantial difference in how the three apps treat video and audio streams. We see that their choice of treatment affects their consumption of hardware resources. Our quantitative user studies confirm the findings of our quantitative measurements. While each platform has its benefits, we find that no app is ideal. A user can choose a suitable platform depending on which of the following, audio, video, or network bandwidth, CPU, or memory are more important.

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Section: Measuring Quantitative Performancementioning

confidence: 99%

Comparison of popular video conferencing apps using client-side measurements on different backhaul networks

Kumar

Nagpal

Naik

et al. 2022

Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

View full text Add to dashboard Cite

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Video Quality Modelling—Comparison of the Classical and Machine Learning Techniques

Klink,

Łuczyński,

Brachmański

2024

Applied Sciences

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The classical objective methods of assessing video quality used so far, apart from their advantages, such as low costs, also have disadvantages. The need to eliminate these defects results in the search for better and better solutions. This article proposes a video quality assessment method based on machine learning using a linear regression model. A set of objective quality assessment metrics was used to train the model. The results obtained show that the prediction of video quality based on a machine learning model gives better results than the objective assessment based on individual metrics. The proposed model showed a strong correlation with the subjective user assessments but also a good fit of the regression function to the empirical data. It is an extension and improvement of the efficiency of the classical methods of objective quality assessment that have been used so far. The solution presented here will allow for a more accurate prediction of the video quality perceived by viewers based on an assessment carried out using a much cheaper, objective method.

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Performance comparison of video quality metrics

Cited by 2 publications

References 0 publications

Comparison of popular video conferencing apps using client-side measurements on different backhaul networks

Comparison of popular video conferencing apps using client-side measurements on different backhaul networks

Video Quality Modelling—Comparison of the Classical and Machine Learning Techniques

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