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Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming

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Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide economically. Therefore, recent efforts have started to target the efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user-perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy, and user's motion within the scene. We determine which of these aspects has more impact on the user's QoE, and to what extent subjective and objective assessments are aligned.

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Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming

  1. 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. 2 Star Wars, anyone?
  3. 3. Dynamic scenes require a significant amount of data 3 4.1 Gb/s 3.8 Gb/s 5.7 Gb/s 5.6 Gb/s
  4. 4. Streaming this scene would require 19.2 Gb/s! 4
  5. 5. We can solve this by using compression 5 4.5 Mb/s 40.4 Mb/s 5.7 Gb/s
  6. 6. HTTP Adaptive Streaming is the de-facto standard 6 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 2 1 3 2 1 3 2 1 3 2 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. 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? 7
  8. 8. Methodology 8
  9. 9. We generated multiple sequences 9
  10. 10. We generated multiple sequences 10
  11. 11. We generated multiple sequences 11
  12. 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 12
  13. 13. Streaming at 20 Mb/s illustrates low video quality 13
  14. 14. Participants were asked to rate the video quality 14 h 4h
  15. 15. RQ1: What is the impact of network and content characteristics on the perceived video quality? 15
  16. 16. Subjects can distinguish between different bitrates 16 20 Mb/s 60 Mb/s 100 Mb/s Unlimited
  17. 17. Viewport prediction allows to improve the observed quality 17 Most recent Clairvoyant
  18. 18. RQ2: How do objective metrics correlate with subjective ratings for the perceived video quality? 18
  19. 19. Clear correlation between objective metrics and MOS scores 19 PSNR SRC 1 SRC 2 SRC 3
  20. 20. Clear correlation between objective metrics and MOS scores 20 SSIM SRC 1 SRC 2 SRC 3
  21. 21. Clear correlation between objective metrics and MOS scores 21 VQM SRC 1 SRC 2 SRC 3
  22. 22. Subjective scores best match with SSIM 22
  23. 23. However, part of the model is explained by the selected SRC 23 SSIM SRC 1 SRC 2 SRC 3
  24. 24. However, part of the model is explained by the selected SRC 24 VQM SRC 1 SRC 2 SRC 3
  25. 25. Conclusions 25
  26. 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 26

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