1. Objective and Subjective QoE Evaluation for
Adaptive Point Cloud Streaming
Jeroen van der Hooft, Maria Torres Vega, Filip De Turck (Ghent University – imec)
Christian Timmerer, Raimund Schatz (AAU Klagenfurt, Bitmovin, AIT)
Ali C. Begen (Özyeğin University)

2. 2
Star Wars, anyone?

3. Dynamic scenes require a significant amount of data
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4.1 Gb/s 3.8 Gb/s 5.7 Gb/s 5.6 Gb/s

4. Streaming this scene would require 19.2 Gb/s!
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5. We can solve this by using compression
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4.5 Mb/s 40.4 Mb/s 5.7 Gb/s

6. HTTP Adaptive Streaming is the de-facto standard
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2
3
Time
Bitrate
3
2
1
3
2
1
3
2
1
3
2
1
3
2
111
Time
Bitrate
1
Bandwidth
2
33
2
1
3
2
1
3
2
1
3
2
1
3
2
11112
33
2
1
3
2
1
3
2
1
3
2
1
3
2
11112
33
2
1
3
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1111Object Time
Bitrate
Bandwidth
1
1
1
1
1
1
2
2
3
3
3
2
2
1
2 2
2
1
2
2

7. Research questions?
RQ1: What is the impact of network and content characteristics on
the perceived video quality?
RQ2: How do objective metrics correlate with subjective ratings for
the perceived video quality?
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8. Methodology
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9. We generated multiple sequences
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10. We generated multiple sequences
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11. We generated multiple sequences
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12. Each of the three videos comes with eight configurations
Bandwidth [Mb/s] Allocation Prediction
20 Visible objects 0
60 Visible objects 0
100 Visible objects 0
20 Visible objects 1
60 Visible objects 1
100 Visible objects 1
60 All objects 0
∞ N/A N/A
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13. Streaming at 20 Mb/s illustrates low video quality
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14. Participants were asked to rate the video quality
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h
4h

15. RQ1: What is the impact of network and content
characteristics on the perceived video quality?
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16. Subjects can distinguish between different bitrates
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20 Mb/s
60 Mb/s
100 Mb/s
Unlimited

17. Viewport prediction allows to improve the observed quality
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Most recent
Clairvoyant

18. RQ2: How do objective metrics correlate with
subjective ratings for the perceived video quality?
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19. Clear correlation between objective metrics and MOS scores
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PSNR
SRC 1
SRC 2
SRC 3

20. Clear correlation between objective metrics and MOS scores
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SSIM
SRC 1
SRC 2
SRC 3

21. Clear correlation between objective metrics and MOS scores
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VQM
SRC 1
SRC 2
SRC 3

22. Subjective scores best match with SSIM
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23. However, part of the model is explained by the selected SRC
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SSIM
SRC 1
SRC 2
SRC 3

24. However, part of the model is explained by the selected SRC
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VQM
SRC 1
SRC 2
SRC 3

25. Conclusions
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26. Conclusions
A single-stimulus method works
RQ1: Impact of network and content characteristics is clear and can be
explained
RQ2: High correlation between objective and subjective metrics, but
observed values need to be adjusted to the considered content
More representative metrics and QoE models are required
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