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Updated: Nov 8, 2025
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Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content
Published on: May 26, 2018
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Towards Perceptually Optimized Adaptive Video Streaming-A Realistic Quality of Experience Database
Summary
We created the LIVE-NFLX-II database to measure video streaming Quality of Experience (QoE). This resource helps develop better streaming algorithms, especially under low bandwidth conditions.
Area of Science:
- Computer Science
- Multimedia Systems
- Human-Computer Interaction
Background:
- Measuring Quality of Experience (QoE) is crucial for video streaming.
- Existing QoE databases and prediction models require enhancement for realistic scenarios.
Purpose of the Study:
- To introduce the LIVE-NFLX-II database for QoE assessment.
- To facilitate the development of advanced video streaming algorithms.
Main Methods:
- Designed the LIVE-NFLX-II database with subjective QoE responses.
- Incorporated content-adaptive encoding and actual network traces.
- Focused on low bandwidth conditions and client-side network variations.
Main Results:
- Studied the impact of bitrate adaptation, network conditions, and content on user experience.
- Evaluated the performance of video quality and QoE prediction models.
- Identified strengths and weaknesses of current QoE models.
Conclusions:
- The LIVE-NFLX-II database provides a valuable tool for QoE research.
- This work will advance perceptually-optimized client adaptation and streaming strategies.
- The database is publicly available to foster further research.
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