Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152986
Title: DeepQoE : a multimodal learning framework for video quality of experience (QoE) prediction
Authors: Zhang, Huaizheng
Dong, Linsen
Gao, Guanyu
Hu, Han
Wen, Yonggang
Guan, Kyle
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Zhang, H., Dong, L., Gao, G., Hu, H., Wen, Y. & Guan, K. (2020). DeepQoE : a multimodal learning framework for video quality of experience (QoE) prediction. IEEE Transactions On Multimedia, 22(12), 3210-3223. https://dx.doi.org/10.1109/TMM.2020.2973828
Journal: IEEE Transactions on Multimedia 
Abstract: Recently, many models have been developed to predict video Quality of Experience (QoE), yet the applicability of these models still faces significant challenges. Firstly, many models rely on features that are unique to a specific dataset and thus lack the capability to generalize. Due to the intricate interactions among these features, a unified representation that is independent of datasets with different modalities is needed. Secondly, existing models often lack the configurability to perform both classification and regression tasks. Thirdly, the sample size of the available datasets to develop these models is often very small, and the impact of limited data on the performance of QoE models has not been adequately addressed. To address these issues, in this work we develop a novel and end-to-end framework termed as DeepQoE. The proposed framework first uses a combination of deep learning techniques, such as word embedding and 3D convolutional neural network (C3D), to extract generalized features. Next, these features are combined and fed into a neural network for representation learning. A learned representation will then serve as input for classification or regression tasks. We evaluate the performance of DeepQoE with three datasets. The results show that for small datasets (e.g., WHU-MVQoE2016 and Live-Netflix Video Database), the performance of state-of-the-art machine learning algorithms is greatly improved by using the QoE representation from DeepQoE (e.g., 35.71% to 44.82%); while for the large dataset (e.g., VideoSet), our DeepQoE framework achieves significant performance improvement in comparison to the best baseline method (90.94% vs. 82.84%). In addition to the much improved performance, DeepQoE has the flexibility to fit different datasets, to learn QoE representation, and to perform both classification and regression problems. We also develop a DeepQoE based adaptive bitrate streaming (ABR) system to verify that our framework can be easily applied to multimedia communication service. The software package of the DeepQoE framework has been released to facilitate the current research on QoE.
URI: https://hdl.handle.net/10356/152986
ISSN: 1520-9210
DOI: 10.1109/TMM.2020.2973828
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Page view(s)

30
Updated on Jan 17, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.