No-reference Bitstream-based Perceptual Quality Assessment of Octree-Lifting Encoded 3D Point Clouds.

No-reference point cloud quality assessment (PCQA) based on bitstreams uses information extracted from the bitstream for quality monitoring at network nodes. We develop a no-reference PCQA model based on bitstreams for the perceived quality assessment of Octree-Lifting coded point clouds. At first, our research explores the essential correlation between subjective visual quality degradation and the texture quantization parameter (TQP) when using lossless geometric coding. Then, we enhance the proposed model by incorporating texture complexity (TC) while taking into account the dependence of perceptual coding distortion on the texture characteristics of a point cloud. We estimate TC by utilizing TQP and calculating the average standard deviation of the Y-component of the attribute value ( Y_ std), both of which are extracted from the bitstream. Then, a texture distortion assessment model is constructed based on TQP and Y_ std. The integration of the texture distortion model with the position quantization scale (PQS) results in the derivation of an overall no-reference bitstream-based PCQA model, named streamPCQ-OL. The findings from the conducted experiments highlight a significant superiority of the proposed model over existing approaches in terms of performance. The dataset and source code will be publicly released and made available for access at https://github.com/qdushl/Waterloo-Point-Cloud-Database-4.0.