Version 1
: Received: 1 February 2024 / Approved: 2 February 2024 / Online: 2 February 2024 (12:32:23 CET)
How to cite:
Selma, T.; Bentaleb, A.; Masud, M.M.; Harous, S. Inference Analysis of Video Quality of Experience in Relation with Face Emotion, Video Advertisement, and ITU-T P.1203. Preprints2024, 2024020167. https://doi.org/10.20944/preprints202402.0167.v1
Selma, T.; Bentaleb, A.; Masud, M.M.; Harous, S. Inference Analysis of Video Quality of Experience in Relation with Face Emotion, Video Advertisement, and ITU-T P.1203. Preprints 2024, 2024020167. https://doi.org/10.20944/preprints202402.0167.v1
Selma, T.; Bentaleb, A.; Masud, M.M.; Harous, S. Inference Analysis of Video Quality of Experience in Relation with Face Emotion, Video Advertisement, and ITU-T P.1203. Preprints2024, 2024020167. https://doi.org/10.20944/preprints202402.0167.v1
APA Style
Selma, T., Bentaleb, A., Masud, M.M., & Harous, S. (2024). Inference Analysis of Video Quality of Experience in Relation with Face Emotion, Video Advertisement, and ITU-T P.1203. Preprints. https://doi.org/10.20944/preprints202402.0167.v1
Chicago/Turabian Style
Selma, T., Mohammad Mehedy Masud and Saad Harous. 2024 "Inference Analysis of Video Quality of Experience in Relation with Face Emotion, Video Advertisement, and ITU-T P.1203" Preprints. https://doi.org/10.20944/preprints202402.0167.v1
Abstract
With end-to-end encryption for video streaming services becoming more popular, network administrators face new challenges in preserving network performance and user experience. Video ads may cause traffic congestion and poor Quality of Experience. Because of the natural variation in user interests and network situations, traditional algorithms for increasing QoE may face limitations. To solve this problem, we suggest a novel method that uses user facial emotion recognition to deduce QoE and study the effect of ads. We use open-access Face Emotion Recognition (FER) datasets and extract facial emotion information from actual observers to train machine learning models. Participants were requested to watch ad videos and provide feedback, which will be used for comparison, training, testing, and validation of our suggested technique. Our tests show that our approach beats the ITU-T P.1203 standard in terms of accuracy by 37.1%. Our method provides a hopeful answer to the problem of increasing user engagement and experience in video streaming services.
Keywords
Quality of Experience, HTTP Adaptive Streaming, Face Emotion Recognition, ITU-T P.1203
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
