The Logarithmic Nature of QoE and the Role of the Weber-Fechner Law in QoE Assessment

By P. Reichl, Sebastian Egger, R. Schatz, A. D'Alconzo. IEEE International Conference on Communications, 2010

311 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “In the literature, simple-regression models with a single feature are most dominant [145, 240, 383, 408], although the collective impact of different QoS parameters was also considered [23, 132].”
From QoS to QoE: A Tutorial on Video Quality Assessment (2015, methodology): “Though prioritizing real-time video trafﬁc over best effort trafﬁc, it is shown that the QoE improvement (quantiﬁed by MS-SSIM index) for the video users only leads to negligible decrease in QoE for best effort users (quantiﬁed by Weber-Fechner Law (WFL)-based web QoE modeling [169], [170]).”
Assessing Blockchain Consensus and Security Mechanisms against the 51% Attack (2019, background): “Our analysis indicates that the consensus mechanisms comprise severe limitations, resulting in a low quality of experience (QoE) of the network participants [25].”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, methodology): “The authors quantify QoE by measurable QoS metrics without end-user involvements, according to the Weber-Fechner Law (WFL) [159]”