Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143270
Title: Deepqoe : a unified framework for learning to predict video QoE
Authors: Zhang, Huaizheng
Hu, Han
Gao, Guanyu
Wen, Yonggang
Guan, Kyle
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Zhang, H., Hu, H., Gao, G., Wen, Y., & Guan, K. (2018). Deepqoe : a unified framework for learning to predict video QoE. Proceedings of 2018 IEEE International Conference on Multimedia and Expo (ICME 2018), 1-6. doi:10.1109/ICME.2018.8486523
Abstract: Motivated by the prowess of deep learning (DL) based techniques in prediction, generalization, and representation learning, we develop a novel framework called DeepQoE to predict video quality of experience (QoE). The end-to-end framework first uses a combination of DL techniques (e.g., word embeddings) to extract generalized features. Next, these features are combined and fed into a neural network for representation learning. Such representations serve as inputs for classification or regression tasks. Evaluating the performance of DeepQoE with two datasets, we show that for the small dataset, the accuracy of all shallow learning algorithms is improved by using the representation derived from DeepQoE. For the large dataset, our DeepQoE framework achieves significant performance improvement in comparison to the best baseline method (90.94% vs. 82.84%). Moreover, DeepQoE, also released as an open source tool, provides video QoE research much-needed flexibility in fitting different datasets, extracting generalized features, and learning representations.
URI: https://hdl.handle.net/10356/143270
ISBN: 978-1-5386-1738-0
DOI: 10.1109/ICME.2018.8486523
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICME.2018.8486523.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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