Inference Analysis of Video Quality of Experience in Relation with Face Emotion, Video Advertisement, and ITU-T P.1203

Tisa Selma, Mohammad Masud, Abdelhak Bentaleb, Saad Harous

Research output: Contribution to journalArticlepeer-review

Abstract

This study introduces an FER-based machine learning framework for real-time QoE assessment in video streaming. This study’s aim is to address the challenges posed by end-to-end encryption and video advertisement while enhancing user QoE. Our proposed framework significantly outperforms the base reference, ITU-T P.1203, by up to 37.1% in terms of accuracy and 21.74% after attribute selection. Our study contributes to the field in two ways. First, we offer a promising solution to enhance user satisfaction in video streaming services via real-time user emotion and user feedback integration, providing a more holistic understanding of user experience. Second, high-quality data collection and insights are offered by collecting real data from diverse regions to minimize any potential biases and provide advertisement placement suggestions.

Original languageEnglish
Article number62
JournalTechnologies
Volume12
Issue number5
DOIs
Publication statusPublished - May 2024

Keywords

  • face emotion recognition
  • HTTP adaptive streaming
  • ITU-T P.1203
  • quality of experience

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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