Bitstream-based no Reference Perceptual Quality Assessment of G-PCC Encoded Point Cloud

‍ ‍In order to realize the real-time monitoring of point cloud quality， this paper proposed a bitstream-based no reference model for perceptual quality assessment of G-PCC encoded point cloud. Firstly， according to the analysis of subjective experimental results， the relationship between perceived quality of point cloud and texture quantization parameters was determined when geometry coding was lossless. Then texture complexity was estimated using texture quantization parameter and texture bitrate. The point cloud quality assessment model was established according to spatial masking effect when geometry coding was lossless. The influence of location quantization scale on sub-sampling quality of point cloud was studied. It was found that the influence of texture quantization parameter and location quantization scale on point cloud quality were independent of each other. Finally， a complete evaluation model of point cloud quality was obtained. The model was tested on WPC4 point cloud database. The SRCC， PLCC and RMSE of the model are 0.9447， 0.9465 and 6.8252， respectively， in the WPC4 point cloud database， indicating that the model has good performance. Compared with the existing objective indexes of GraphSIM with the best performance， the PLCC and SRCC distribution of this model index increased by 0.0223 and 0.0238， and RMSE decreased by 1.1898.