Back to articles
Volume: 34 | Article ID: IQSP-385
Learning-based 3D point cloud quality assessment using a support vector regressor
  DOI :  10.2352/EI.2022.34.9.IQSP-385  Published OnlineJanuary 2022

Recent advances in capture technologies have increased the production of 3D content in the form of Point Clouds (PCs). The perceived quality of such data can be impacted by typical processing including acquisition, compression, transmission, visualization, etc. In this paper, we propose a learning-based method that efficiently predicts the quality of distorted PCs through a set of features extracted from the reference PC and its degraded version. The quality index is obtained here by combining the considered features using a Support Vector Regression (SVR) model. The performance contribution of each considered feature and their combination are compared. We then discuss the experimental results obtained in the context of state-of-the-art methods using 2 publicly available datasets. We also evaluate the ability of our method to predict unknown PCs through a cross-dataset evaluation. The results show the relevance of introducing a learning step to merge features for the quality assessment of such data.

Subject Areas :
Views 28
Downloads 7
 articleview.views 28
 articleview.downloads 7
  Cite this article 

Aladine Chetouani, Maurice Quach, Giuseppe Valenzise, Frédéric Dufaux, "Learning-based 3D point cloud quality assessment using a support vector regressorin Electronic Imaging,  2022,  pp 385-1 - 385-5,

 Copy citation
  Copyright statement 
Copyright © 2022, Society for Imaging Science and Technology 2022
Electronic Imaging
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane, Springfield, VA 22151 USA