Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2023 15th International Conference on Quality of Multimedia Experience (QoMEX) |
| Seiten | 135-140 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9798350311730 |
| Publikationsstatus | Veröffentlicht - 20 Juni 2023 |
Abstract
There is growing interest in point cloud content due to its central role in the creation and provision of interactive and immersive user experiences for extended reality applications. However, it is impractical to stream uncompressed point cloud sequences over communication networks to end systems because of their high throughput and low latency requirements. Several novel compression methods have been developed for efficient storage and adaptive delivery of point cloud content. However, these methods primarily focus on data metrics and neglect the influence on the actual Quality of Experience (QoE). In this paper, we conduct a user study with 102 participants to analyze the QoE of point cloud sequences and develop a QoE model that can enhance the quality of point cloud content distribution under dynamic network conditions. Our analysis is based on user opinions regarding two representative point cloud sequences, three different frame rates, three viewing distances, and two state-of-the-art point cloud compression libraries, Draco and V-PCC. The results indicate that the proposed models can accurately predict the users' quality perception, with frame rate being the most dominant QoE factor.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Ingenieurwesen (insg.)
- Medientechnik
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- BibTex
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2023 15th International Conference on Quality of Multimedia Experience (QoMEX). 2023. S. 135-140.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Modeling Quality of Experience for Compressed Point Cloud Sequences based on a Subjective Study
AU - Weil, Jannis
AU - Alkhalili, Yassin
AU - Tahir, Anam
AU - Gruczyk, Thomas
AU - Meuser, Tobias
AU - Mu, Mu
AU - Koeppl, Heinz
AU - Mauthe, Andreas
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2023/6/20
Y1 - 2023/6/20
N2 - There is growing interest in point cloud content due to its central role in the creation and provision of interactive and immersive user experiences for extended reality applications. However, it is impractical to stream uncompressed point cloud sequences over communication networks to end systems because of their high throughput and low latency requirements. Several novel compression methods have been developed for efficient storage and adaptive delivery of point cloud content. However, these methods primarily focus on data metrics and neglect the influence on the actual Quality of Experience (QoE). In this paper, we conduct a user study with 102 participants to analyze the QoE of point cloud sequences and develop a QoE model that can enhance the quality of point cloud content distribution under dynamic network conditions. Our analysis is based on user opinions regarding two representative point cloud sequences, three different frame rates, three viewing distances, and two state-of-the-art point cloud compression libraries, Draco and V-PCC. The results indicate that the proposed models can accurately predict the users' quality perception, with frame rate being the most dominant QoE factor.
AB - There is growing interest in point cloud content due to its central role in the creation and provision of interactive and immersive user experiences for extended reality applications. However, it is impractical to stream uncompressed point cloud sequences over communication networks to end systems because of their high throughput and low latency requirements. Several novel compression methods have been developed for efficient storage and adaptive delivery of point cloud content. However, these methods primarily focus on data metrics and neglect the influence on the actual Quality of Experience (QoE). In this paper, we conduct a user study with 102 participants to analyze the QoE of point cloud sequences and develop a QoE model that can enhance the quality of point cloud content distribution under dynamic network conditions. Our analysis is based on user opinions regarding two representative point cloud sequences, three different frame rates, three viewing distances, and two state-of-the-art point cloud compression libraries, Draco and V-PCC. The results indicate that the proposed models can accurately predict the users' quality perception, with frame rate being the most dominant QoE factor.
KW - machine learning
KW - point clouds
KW - quality of experience
KW - subjective user study
UR - http://www.scopus.com/inward/record.url?scp=85167337579&partnerID=8YFLogxK
U2 - 10.1109/qomex58391.2023.10178579
DO - 10.1109/qomex58391.2023.10178579
M3 - Conference contribution
SP - 135
EP - 140
BT - 2023 15th International Conference on Quality of Multimedia Experience (QoMEX)
ER -