Perceptual Quality Assessment of Octree-RAHT Encoded 3D Point Clouds

TL;DR

This paper introduces a no-reference point cloud quality assessment model for Octree-RAHT encoded 3D point clouds, utilizing a new database and stream parsing to enable real-time quality monitoring with high accuracy and low computational cost.

Contribution

It establishes the first dedicated quality assessment database for Octree-RAHT encoding and proposes a novel bitstream-based model that predicts quality without full decoding.

Findings

The model outperforms existing methods in accuracy.

It achieves high performance with low computational complexity.

The database and code are publicly available for further research.

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 focus on the PCQA problem dedicated to Octree-RAHT encoding mode. First, to address the issue that existing PCQA databases have a small scale and limited distortion levels, we establish the WPC5.0 database which is the first one dedicated to Octree-RAHT encoding mode with a scale of 400 distorted point clouds (PCs) including 4 geometric multiplied by 5 attitude distortion levels. Then, we propose the first PCQA model dedicated to Octree-RAHT encoding mode by parsing PC bitstreams without full decoding. The model introduces texture bitrate (TBPP) to predict texture complexity (TC) and further derives the texture distortion factor. In addition, the Geometric Quantization Parameter (PQS) is used to estimate the geometric distortion factor, which is then integrated into the model along with the texture distortion factor to obtain the proposed PCQA model named streamPCQ-OR. The proposed model has been compared with other advanced PCQA methods on the WPC5.0, BASICS and M-PCCD databases, and experimental results show that our model has excellent performance while having very low computational complexity, providing a reliable choice for time-critical applications. To facilitate subsequent research, the database and source code will be publicly released at https://github.com/qdushl/Waterloo-Point-Cloud-Database-5.0.