Reduced Reference Quality Assessment for Point Cloud Compression

TL;DR

This paper introduces R-PCQA, a reduced reference model that assesses point cloud quality by analyzing compression parameters, demonstrating reliable performance and strong generalization across datasets.

Contribution

The paper presents a novel RR-PCQA model that correlates quantization steps with perceptual quality, improving point cloud quality assessment for various compression methods.

Findings

R-PCQA achieves high correlation with subjective quality scores.

The model generalizes well across different datasets.

It effectively quantifies distortions from attribute and geometry compression.

Abstract

In this paper, we propose a reduced reference (RR) point cloud quality assessment (PCQA) model named R-PCQA to quantify the distortions introduced by the lossy compression. Specifically, we use the attribute and geometry quantization steps of different compression methods (i.e., V-PCC, G-PCC and AVS) to infer the point cloud quality, assuming that the point clouds have no other distortions before compression. First, we analyze the compression distortion of point clouds under separate attribute compression and geometry compression to avoid their mutual masking, for which we consider 5 point clouds as references to generate a compression dataset (PCCQA) containing independent attribute compression and geometry compression samples. Then, we develop the proposed R-PCQA via fitting the relationship between the quantization steps and the perceptual quality. We evaluate the performance of R-PCQA on both the established dataset and another independent dataset. The results demonstrate that the proposed R-PCQA can exhibit reliable performance and high generalization ability.