Perceptual Quality Assessment of Colored 3D Point Clouds

TL;DR

This paper introduces a new large database and a novel objective quality assessment model for colored 3D point clouds, demonstrating improved correlation with human subjective ratings over existing models.

Contribution

The work provides a comprehensive database and a new information content weighted structural similarity model for better perceptual quality assessment of colored 3D point clouds.

Findings

Existing models have limited success in predicting subjective quality.

The proposed model significantly outperforms existing PCQA models.

The database is publicly available for further research.

Abstract

The real-world applications of 3D point clouds have been growing rapidly in recent years, but not much effective work has been dedicated to perceptual quality assessment of colored 3D point clouds. In this work, we first build a large 3D point cloud database for subjective and objective quality assessment of point clouds. We construct 20 high quality, realistic, and omni-directional point clouds of diverse contents. We then apply downsampling, Gaussian noise, and three types of compression algorithms to create 740 distorted point clouds. We carry out a subjective experiment to evaluate the quality of distorted point clouds. Our statistical analysis finds that existing objective point cloud quality assessment (PCQA) models only achieve limited success in predicting subjective quality ratings. We propose a novel objective PCQA model based on the principle of information content weighted structural similarity. Our experimental results show that the proposed model well correlates with subjective opinions and significantly outperforms the existing PCQA models. The database has been made publicly available to facilitate reproducible research at https://github.com/qdushl/Waterloo-Point-Cloud-Database.