Continuous QoE Prediction Based on WaveNet

Phan Xuan Tan, Tho Nguyen Duc, Chanh Minh Tran, Eiji Kamioka

研究成果: Conference contribution

抄録

Continuous QoE prediction is crucial in the purpose of maximizing viewer satisfaction, by which video service providers could improve the revenue. Continuously predicting QoE is challenging since it requires QoE models that are capable of capturing the complex dependencies among QoE influence factors. The existing approaches that utilize Long-Short-Term-Memory (LSTM) network successfully model such long-term dependencies, providing the superior QoE prediction performance. However, the inherent drawback of sequential computing of LSTM will result in high computational cost in training and prediction tasks. Recently, WaveNet, a deep neural network for generating raw audio waveform, has been introduced. Immediately, it gains a great attention since it successfully leverages the characteristic of parallel computing of causal convolution and dilated convolution to deal with time-series data (e.g., audio signal). Being inspired by the success of WaveNet, in this paper, we propose WaveNet-based QoE model for continuous QoE prediction in video streaming services. The model is trained and tested upon on two publicly available databases, namely, LFOVIA Video QoE and LIVE Mobile Stall Video II. The experimental results demonstrate that the proposed model outperforms the baselines models in terms of processing time, while maintaining sufficient accuracy.

本文言語English
ホスト出版物のタイトルProceedings of the 2020 12th International Conference on Computer and Automation Engineering, ICCAE 2020
出版社Association for Computing Machinery
ページ80-84
ページ数5
ISBN(電子版)9781450376785
DOI
出版ステータスPublished - 2020 2 14
イベント12th International Conference on Computer and Automation Engineering, ICCAE 2020 - Sydney, Australia
継続期間: 2020 2 142020 2 16

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

Conference12th International Conference on Computer and Automation Engineering, ICCAE 2020
国/地域Australia
CitySydney
Period20/2/1420/2/16

ASJC Scopus subject areas

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信

フィンガープリント

「Continuous QoE Prediction Based on WaveNet」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル