Quantification of YouTube QoE via Crowdsourcing
By Tobias Hossfeld, Michael Seufert, Matthias Hirth, Thomas Zinner, Phuoc Tran-Gia, R. Schatz. IEEE International Symposium on Multimedia, 2011
383 citations recorded by the source corpus.
Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/30ee6390c35e9939791eb1f89a9107c7d8d25469
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Science Applications
In Computer Vision and Pattern Recognition
Citations in the corpus
- A web based subjective evaluation platform (2013, result): “In contrast to the well-known laboratory experiments where the experiment is conducted in a controlled environment, crowdsourced experiments are not controlled and, thus, may result in cheating, unintended environment, no possibility to ask questions, etc. [3].”
- A Survey on Quality of Experience of HTTP Adaptive Streaming (2015, positive): “The authors of [4] present a model for mapping regular stalling patterns to MOS.”
- Robustness in speech quality assessment and temporal training expiry in mobile crowdsourcing environments (2015, result): “Focusing on quality assessment of an online video service, [9] compares results from two laboratory studies and different crowdsourcing studies.”
- Effect of trapping questions on the reliability of speech quality judgments in a crowdsourcing paradigm (2015, methodology): “In the context of QoE, researchers have used additional methods like content questions and consistency tests [9], [10].”