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Comparative Evaluation of Temporal Pooling Methods for No-Reference Quality Assessment of Dynamic Point Clouds by Pedro G. Freitas; Giovani D. Lucafo; Mateus Gonçalves; Johann Homonnai; Rafael Diniz; Mylène C.Q. Farias is a scholarly article available to read on EtoBox.

What is Comparative Evaluation of Temporal Pooling Methods for No-Reference Quality Assessment of Dynamic Point Clouds about?

Point Cloud Quality Assessment (PCQA) has become an important task in immersive multimedia since it is fundamental for improving computer graphics applications and ensuring the best Quality of Experience (QoE) for the end user. In recent years, the field of PCQA has made exemplary progress, with state-of-the-art methods achieving better predictive performance at lower computational complexity. However, most of this progress was made using Full-Reference (FR) metrics. Since, in many cases, the reference point cloud is not available, the design of No-Reference (NR) methods has become increasingly important. In this paper, we investigate the suitability of geometric-aware texture descriptors to blindly assess the quality of colored Dynamic Point Cloud (DPC). The proposed metric first uses a descriptor to extract features of the assessed Point Cloud (PC) frames. Then, the descriptor statistics are used to extract quality-aware features. Finally, a machine learning algorithm is employed to regress the quality-aware features into visual quality scores, and these scores are aggregated using a temporal pooling function. Then we study the effects of different temporal pooling strategies on

Author

Pedro G. Freitas; Giovani D. Lucafo; Mateus Gonçalves; Johann Homonnai; Rafael Diniz; Mylène C.Q. Farias

Publisher

ACM

Published

2022

Language

EN