  In this paper, we propose a reduced reference (RR) point cloud quality
assessment (PCQA) model named R-PCQA to quantify the distortions introduced by
the lossy compression. Specifically, we use the attribute and geometry
quantization steps of different compression methods (i.e., V-PCC, G-PCC and
AVS) to infer the point cloud quality, assuming that the point clouds have no
other distortions before compression. First, we analyze the compression
distortion of point clouds under separate attribute compression and geometry
compression to avoid their mutual masking, for which we consider 5 point clouds
as references to generate a compression dataset (PCCQA) containing independent
attribute compression and geometry compression samples. Then, we develop the
proposed R-PCQA via fitting the relationship between the quantization steps and
the perceptual quality. We evaluate the performance of R-PCQA on both the
established dataset and another independent dataset. The results demonstrate
that the proposed R-PCQA can exhibit reliable performance and high
generalization ability.
