MCQoE

Citation Author(s):
Hao
Yang
Submitted by:
hao yang
Last updated:
Fri, 01/03/2025 - 09:55
DOI:
10.21227/7etx-4m30
Data Format:
*.xlsx; *.mp4
License:
Creative Commons Attribution
9 Views
Categories:
Image Processing
Communications
Keywords:
Subjective video quality experiment, adaptive video streaming, quality-of-experience assessment
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Abstract 

Large-screen 4K TVs (75 inches or larger) are experiencing a surge in popularity, providing unparalleled immersive experiences. Consequently, there has been a significant shift in user behavior towards consuming more video content on TVs. Investigating Quality of Experience (QoE) on large screens is paramount, given its critical role in upgrading the overall satisfaction and user engagement associated with video streaming services.

However, research on QoE for large-screen TVs remains not well-investigated and has been largely overlooked by academia and industry. In this paper, we first build an open-source, multi-device continuous QoE dataset named MCQoE by conducting a large-scale subjective experiment to analyze QoE variations among different screen-size devices.

Instructions: 

The MCQoE dataset comprises 21 videos, including 7 reference videos (undistorted) and 14 corresponding distorted videos. All reference videos are originally downloaded from Bilibili, one of the largest User-Generated Content (UGC) platforms in China. The principle of video selection is to build a diverse video category, including Dance, Sport, Game, Landscape, Singer, Wallpaper, and Commentary.

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