2021/08/05 by Alireza Javaheri, Catarina Brites, Javaheri, Alireza +5
Computer Science · Earth and Planetary Sciences · Engineering · #3D Shape Modeling and Analysis #3D Surveying and Cultural Heritage #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Multimedia (cs.MM) #Optical measurement and interference techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2108.02481
openalex publication_date 2021/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Point cloud coding solutions have been recently standardized to address the needs of multiple application scenarios. The design and assessment of point cloud coding methods require reliable objective quality metrics to evaluate the level of degradation introduced by compression or any other type of processing. Several point cloud objective quality metrics has been recently proposed to reliable estimate human perceived quality, including the so-called projection-based metrics. In this context, this paper proposes a joint geometry and color projection-based point cloud objective quality metric which solves the critical weakness of this type of quality metrics, i.e., the misalignment between the reference and degraded projected images. Moreover, the proposed point cloud quality metric exploits the best performing 2D quality metrics in the literature to assess the quality of the projected images. The experimental results show that the proposed projection-based quality metric offers the best subjective-objective correlation performance in comparison with other metrics in the literature. The Pearson correlation gains regarding D1-PSNR and D2-PSNR metrics are 17% and 14.2 when data with all coding degradations is considered.