Joint Geometry and Color Projection-Based Point Cloud Quality Metric

Point cloud coding solutions have been recently standardized to address the needs of multiple application scenarios. The design and assessment point methods require reliable objective quality metrics evaluate level degradation introduced by compression or any other type processing. Several proposed reliably estimate human perceived quality, including so-called projection-based metrics. In this context, paper proposes a joint geometry color metric which solves critical weakness metrics, i.e., misalignment between reference degraded projected images. Moreover, exploits best performing 2D in literature assess experimental results show that offers subjective-objective correlation performance comparison with literature. Pearson gains regarding D1-PSNR D2-PSNR range ~5% ~70% on three different datasets when data all degradations is considered.