HTTP Adaptive Streaming QoE Estimation with ITU-T Rec. P.1203 – Open Databases and Software
Werner Robitza, Steve Göring, Alexander Raake, David Lindegren, Gunnar Heikkilä, Jörgen Gustafsson, Peter List, Bernhard Feiten, Ulf Wüstenhagen, Marie Neige-Garcia, Kazuhisa Yamagishi, Simon Broom
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/itu-p1203/itu-p1203none★ 115
Abstract
This paper describes an open dataset and software for ITU-T Rec. P.1203. As the first standardized Quality of Experience model for audiovisual HTTP Adaptive Streaming (HAS), it has been extensively trained and validated on over a thousand audiovisual sequences containing HAS-typical effects (such as stalling, coding artifacts, quality switches). Our dataset comprises four of the 30 official subjective databases at a bitstream feature level. The paper also includes subjective results and the model performance. Our software for the standard was made available to the public, too, and it is used for all the analyses presented. Among other previously unpublished details , we show the significant performance improvements of using bitstream-based models over metadata-based ones for video quality analysis, and the robustness of combining classical models with machine-learning-based approaches for estimating user QoE.