000255422 001__ 255422
000255422 005__ 20190812210022.0
000255422 0247_ $$a10.1109/PCS.2018.8456252$$2doi
000255422 020__ $$a978-1-5386-4160-6
000255422 02470 $$2DOI$$a10.1109/PCS.2018.8456252
000255422 037__ $$aCONF
000255422 245__ $$aBenchmarking of Objective Quality Metrics for Colorless Point Clouds
000255422 260__ $$c2018
000255422 269__ $$a2018
000255422 300__ $$a5
000255422 336__ $$aConference Papers
000255422 500__ $$a"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."
000255422 520__ $$aRecent advances in depth sensing and display technologies, along with the significant growth of interest for augmented and virtual reality applications, lay the foundation for the rapid evolution of applications that provide immersive experiences. In such applications, advanced content representations are required in order to increase the engagement of the user with the displayed imageries. Point clouds have emerged as a promising solution to this aim, due to their efficiency in capturing, storing, delivering and rendering of 3D immersive contents. As in any type of imaging, the evaluation of point clouds in terms of visual quality is essential. In this paper, benchmarking results of the state-of-the-art objective metrics in geometry-only point clouds are reported and analyzed under two different types of geometry degradations, namely Gaussian noise and octree-based compression. Human ratings obtained from two subjective experiments are used as the ground truth. Our results show that most objective quality metrics perform well in the presence of noise, whereas one particular method has high predictive power and outperforms the others after octree-based encoding.
000255422 6531_ $$apoint cloud
000255422 6531_ $$abenchmarking
000255422 6531_ $$aquality assessment
000255422 6531_ $$aquality metrics
000255422 700__ $$g274471$$aAlexiou, Evangelos$$0250078
000255422 700__ $$0240223$$aEbrahimi, Touradj$$g105043
000255422 7112_ $$dJune 24-27, 2018$$cSan Francisco, California, USA$$a2018 Picture Coding Symposium (PCS)
000255422 773__ $$t2018 Picture Coding Symposium (PCS)
000255422 8564_ $$uhttps://infoscience.epfl.ch/record/255422/files/PCS-2018.pdf$$s392398
000255422 8560_ $$fevangelos.alexiou@epfl.ch
000255422 909C0 $$xUS02700$$pMMSPL$$mtouradj.ebrahimi@epfl.ch$$0252077$$xU11889
000255422 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.epfl.ch:255422
000255422 960__ $$aevangelos.alexiou@epfl.ch
000255422 961__ $$apierre.devaud@epfl.ch
000255422 973__ $$aEPFL$$rREVIEWED
000255422 980__ $$aCONF
000255422 981__ $$aoverwrite