TY - GEN
T1 - Evaluation of point cloud features for no-reference visual quality assessment
AU - Smitskamp, Gwennan
AU - Viola, Irene
AU - Cesar, Pablo
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2023
Y1 - 2023
N2 - The development and widespread adoption of immersive XR applications has led to a renewed interest in representations that are capable of reproducing real-world objects and scenes with high fidelity. Among such representations, point clouds have attracted the interest of industry and academia alike, and new compression solutions have been developed to facilitate their adoption in mainstream applications. To ensure the best quality of experience for the end-user in limited bandwidth scenarios, new full-reference objective quality metrics have been proposed, promoting features designed specifically for point cloud contents. However, the performance of such features to predict the quality of point cloud contents when the reference is not available is largely unexplored. In this paper, we evaluate the performance of features commonly used to model point cloud distortions in a no-reference framework. The obtained features are integrated into a quality value through a support vector regression model. Results demonstrate the potential of full-reference features for no-reference assessment.
AB - The development and widespread adoption of immersive XR applications has led to a renewed interest in representations that are capable of reproducing real-world objects and scenes with high fidelity. Among such representations, point clouds have attracted the interest of industry and academia alike, and new compression solutions have been developed to facilitate their adoption in mainstream applications. To ensure the best quality of experience for the end-user in limited bandwidth scenarios, new full-reference objective quality metrics have been proposed, promoting features designed specifically for point cloud contents. However, the performance of such features to predict the quality of point cloud contents when the reference is not available is largely unexplored. In this paper, we evaluate the performance of features commonly used to model point cloud distortions in a no-reference framework. The obtained features are integrated into a quality value through a support vector regression model. Results demonstrate the potential of full-reference features for no-reference assessment.
KW - 3D model quality assessment
KW - colored point cloud
KW - no-reference quality assessment
UR - http://www.scopus.com/inward/record.url?scp=85167344278&partnerID=8YFLogxK
U2 - 10.1109/QoMEX58391.2023.10178459
DO - 10.1109/QoMEX58391.2023.10178459
M3 - Conference contribution
AN - SCOPUS:85167344278
T3 - 2023 15th International Conference on Quality of Multimedia Experience, QoMEX 2023
SP - 147
EP - 152
BT - 2023 15th International Conference on Quality of Multimedia Experience, QoMEX 2023
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 15th International Conference on Quality of Multimedia Experience, QoMEX 2023
Y2 - 20 June 2023 through 22 June 2023
ER -