Benchmarking of Objective Quality Metrics for Colorless Point Clouds

Recent advances in depth sensing and display technologies, along with the significant growth of interest for augmented and virtual reality applications, lay the foundation for the rapid evolution of applications that provide immersive experiences. In such applications, advanced content representations are required in order to increase the engagement of the user with the displayed imageries. Point clouds have emerged as a promising solution to this aim, due to their efficiency in capturing, storing, delivering and rendering of 3D immersive contents. As in any type of imaging, the evaluation of point clouds in terms of visual quality is essential. In this paper, benchmarking results of the state-of-the-art objective metrics in geometry-only point clouds are reported and analyzed under two different types of geometry degradations, namely Gaussian noise and octree-based compression. Human ratings obtained from two subjective experiments are used as the ground truth. Our results show that most objective quality metrics perform well in the presence of noise, whereas one particular method has high predictive power and outperforms the others after octree-based encoding.

Published in:
2018 Picture Coding Symposium (PCS)
Presented at:
Picture Coding Symposium (PCS), San Francisco, California, USA, June 24-27, 2018
New York, IEEE
Copyright 2018 IEEE. Published in the 2018 33rd Picture Coding Workshop (PCS 2018), 24-27 June 2018 in San Francisco, United States. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966.

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 Record created 2018-05-24, last modified 2020-10-25

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