Energy-Adaptive Bitstream-Layer Model for Perceptual Quality Assessment of V-PCC Encoded 3D Point Clouds.

The scope of point cloud (PC) applications is expanding. We propose a no-reference bitstream-layer quality assessment model that eliminates the need for full decoding of the PC, providing quality evaluation scores during the V-PCC decoding process. Specifically, we illustrate the relationship between content diversity (CD) and perceptual coding distortion in lossless geometric coding. Subsequently, we model attribute distortion by predicting CD using transform energy (TE) and texture quantization parameter (TQP). By combining the geometric distortion model with geometry quantization parameters (GQP) and the attribute distortion model, we derive comprehensive quality prediction results. Our experimental results on four PC databases (WPC2.0, M-PCCD, VSENSE VVDB and VSENSE VVDB2) show that the proposed energy-adaptive bitstream-layer model (EABL) delivers competitive quality prediction performance in comparison with existing full-reference, reduced-reference and no-reference PC quality assessment models that require full decoding, and meanwhile exhibits large speed advantage. The source code will be made publicly available for repeatability research at https://github.com/arthas-sws/EABL_model.