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Christian Keimel

Researcher at Technische Universität München

Publications -  43
Citations -  1735

Christian Keimel is an academic researcher from Technische Universität München. The author has contributed to research in topics: Video quality & Crowdsourcing. The author has an hindex of 18, co-authored 43 publications receiving 1552 citations.

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Journal ArticleDOI

Best Practices for QoE Crowdtesting: QoE Assessment With Crowdsourcing

TL;DR: The focus of this article is on the issue of reliability and the use of video quality assessment as an example for the proposed best practices, showing that the recommended two-stage QoE crowdtesting design leads to more reliable results.
Proceedings ArticleDOI

QualityCrowd — A framework for crowd-based quality evaluation

TL;DR: This contribution proposes the QualityCrowd framework, which allows codec independent quality assessment with a simple web interface, usable with common web browsers, and compared the results from an online subjective test using this framework with theresults from a test in a standardized environment, showing that qualityCrowd delivers equivalent results within the acceptable inter-lab correlation.
Proceedings ArticleDOI

Crowdsourcing-based multimedia subjective evaluations: a case study on image recognizability and aesthetic appeal

TL;DR: High correlation between crowdsourcing and lab scores for recognizability but not for aesthetic appeal is found, indicating that crowdsourcing can be used for QoE subjective assessments as long as the workers' tasks are designed with extreme care to avoid misinterpretations.

Best Practices and Recommendations for Crowdsourced QoE - Lessons learned from the Qualinet Task Force Crowdsourcing

TL;DR: This white paper summarizes the recommendations and best practices for crowdsourced quality assessment of multimedia applications from the Qualinet Task Force on “Crowdsourcing” and resulted from the experience in designing, implementing, and conducting crowdsourcing experiments as well as the analysis of the crowdsourced user ratings and context data.