Multi-modal No-Reference Objective Quality Assessment Method for Point Cloud Videos
Authors: 
Xiaolei Chen, Yuru Zhang, Runyu WenAuthors Info & Claims
Xiaolei Chen, Yuru Zhang, Runyu WenAuthors Info & ClaimsPages 558 - 564
Editorial Notes
The authors have requested minor, non-substantive changes to the Version of Record and, in accordance with ACM policies, a Corrected Version of Record was published on July 9, 2024. For reference purposes, the VoR may still be accessed via the Supplemental Material section on this page.
Abstract
This paper proposes a multi-modal reference-free objective quality assessment method for point cloud videos. The method extracts spatial and temporal features of distorted point cloud videos through three network branches, namely point cloud, projection, and video, respectively, and then fuses the spatio-temporal features using a multi-modal attention mechanism to improve the accuracy and generalization ability of point cloud video quality prediction. In this paper, sufficient experiments are conducted on a self-constructed database consisting of 36 distorted point cloud video sequences, and the experimental results show that the multi-modal quality assessment method proposed in this paper outperforms the current state-of-the-art 11 unimodal and bimodal methods, and can further improve the quality assessment performance.
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VoR for "Multi-modal No-Reference Objective Quality Assessment Method for Point Cloud Videos" by Chen et al., Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering (EITCE '23).
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- Multi-modal No-Reference Objective Quality Assessment Method for Point Cloud Videos
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Published In
October 2023
1809 pages
ISBN:9798400708305
DOI:10.1145/3650400
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Published: 17 April 2024
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EITCE 2023
EITCE 2023: 2023 7th International Conference on Electronic Information Technology and Computer Engineering
October 20 - 22, 2023
Xiamen, China
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