Layered Projection-Based Quality Assessment of 3D Point Clouds

• Main Authors: Tianxin Chen, Chunyi Long, Honglei Su, Lijun Chen, Jieru Chi, Zhenkuan Pan, Huan Yang, Yuxin Liu
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Layered projection; image quality assessment; 3D point cloud; point cloud quality assessment
• Online Access: https://ieeexplore.ieee.org/document/9448078/

Point clouds are subject to various distortions during point cloud processing missions, any of which may lead to quality degradation. Consequently, predicting point cloud quality has attracted a lot of attention. In this paper, a layered projection-based point cloud quality metric (LP-PCQM) is proposed. We layer the distorted point cloud and its original version firstly and then extract the geometry and color features of layers. The geometry feature is obtained using the projection-based method and the color features are extracted upon RGB by using the point-based method. Finally, the LP-PCQM is a weighted linear combination of an optimal subset of these pooled geometry and color features of layers. To verify the performance of LP-PCQM, we compare it with other eight metrics including both point-based metrics and projection-based metrics on the WPC, SJTU-PCQA, and ICIP2020 database respectively. Experimental results show that the proposed metric exhibits better and more robust performance.