  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.
