No-reference Bitstream-layer Model for Perceptual Quality Assessment of V-PCC Encoded Point Clouds
PubDate: May 2022
Teams: Qingdao University；Shandong University；De Montfort University
Writers: Qi Liu; Honglei Su; Tianxin Chen; Hui Yuan; Raouf Hamzaoui
No-reference bitstream-layer models for point cloud quality assessment (PCQA) use the information extracted from a bitstream for real-time and nonintrusive quality monitoring. We propose a no-reference bitstream-layer model for the perceptual quality assessment of video-based point cloud compression (V-PCC) encoded point clouds. First, we describe the fundamental relationship between perceptual coding distortion and the texture quantization parameter (TQP) when geometry encoding is lossless. Then, we incorporate the texture complexity (TC) into the proposed model while considering the fact that the perceptual coding distortion of a point cloud depends on the texture characteristics. TC is estimated using TQP and the texture bitrate per pixel (TBPP), both of which are extracted from the compressed bitstream without resorting to complete decoding. Then, we construct a texture distortion assessment model upon TQP and TBPP. By combining this texture distortion model with the geometry quantization parameter (GQP), we obtain an overall no-reference bitstream-layer PCQA model that we call bitstreamPCQ. Experimental results show that the proposed model markedly outperforms existing models in terms of widely used performance criteria, including the Pearson linear correlation coefficient (PLCC), the Spearman rank order correlation coefficient (SRCC) and the root mean square error (RMSE). The dataset developed in this study is publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-3.0.