Year: 2014
Type: article
Abstract: Newly developed hypertext transfer protocol (HTTP)-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' quality of experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for online TVSQ prediction in HTTP-based streaming.
Authors: Chao Chen, Lark Kwon Choi, Gustavo de Veciana, Constantine Caramanis, Robert W. Heath, Alan C. Bovik
Institution: The University of Texas at Austin
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2013131615