DeepQoE: A unified Framework for Learning to Predict Video QoE
This provides a flexible tool for video QoE research, but it is incremental as it applies existing deep learning techniques to a specific domain.
The authors tackled the problem of predicting video quality of experience (QoE) by developing DeepQoE, a deep learning framework that extracts generalized features and learns representations, achieving a performance improvement from 82.84% to 90.94% accuracy compared to the best baseline on a large dataset.
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 algorithm 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.