Perceptual Quality Assessment of Trisoup-Lifting Encoded 3D Point Clouds

TL;DR

This paper introduces a no-reference point cloud quality assessment model for Trisoup-Lifting encoded 3D point clouds that analyzes bitstreams without full decoding, enabling real-time quality monitoring.

Contribution

It develops the first PCQA model for Trisoup-Lifting encoded point clouds based on bitstream analysis and introduces a new large database for evaluation.

Findings

The proposed streamPCQ-TL model outperforms existing metrics in accuracy.

It demonstrates robustness and efficiency in quality assessment.

The model is suitable for real-time applications due to low computational cost.

Abstract

No-reference bitstream-layer point cloud quality assessment (PCQA) can be deployed without full decoding at any network node to achieve real-time quality monitoring. In this work, we develop the first PCQA model dedicated to Trisoup-Lifting encoded 3D point clouds by analyzing bitstreams without full decoding. Specifically, we investigate the relationship among texture bitrate per point (TBPP), texture complexity (TC) and texture quantization parameter (TQP) while geometry encoding is lossless. Subsequently, we estimate TC by utilizing TQP and TBPP. Then, we establish a texture distortion evaluation model based on TC, TBPP and TQP. Ultimately, by integrating this texture distortion model with a geometry attenuation factor, a function of trisoupNodeSizeLog2 (tNSL), we acquire a comprehensive NR bitstream-layer PCQA model named streamPCQ-TL. In addition, this work establishes a database named WPC6.0, the first and largest PCQA database dedicated to Trisoup-Lifting encoding mode, encompassing 400 distorted point clouds with both 4 geometric multiplied by 5 texture distortion levels. Experiment results on M-PCCD, ICIP2020 and the proposed WPC6.0 database suggest that the proposed streamPCQ-TL model exhibits robust and notable performance in contrast to existing advanced PCQA metrics, particularly in terms of computational cost. The dataset and source code will be publicly released at https://github.com/qdushl/Waterloo-Point-Cloud-Database-6.0