Full-reference Point Cloud Quality Assessment Using Spectral Graph Wavelets

TL;DR

This paper presents a novel full-reference point cloud quality assessment method using spectral graph wavelets, which improves correlation with subjective scores by combining SGW-based metrics with conventional ones.

Contribution

It introduces the first use of spectral graph wavelets for point cloud quality assessment, enhancing accuracy through a combined metric approach.

Findings

SGW-based metrics outperform conventional metrics in correlation with subjective scores.

The combined support vector regression model improves PCQA accuracy.

First application of spectral graph wavelets in PCQA.

Abstract

Point clouds in 3D applications frequently experience quality degradation during processing, e.g., scanning and compression. Reliable point cloud quality assessment (PCQA) is important for developing compression algorithms with good bitrate-quality trade-offs and techniques for quality improvement (e.g., denoising). This paper introduces a full-reference (FR) PCQA method utilizing spectral graph wavelets (SGWs). First, we propose novel SGW-based PCQA metrics that compare SGW coefficients of coordinate and color signals between reference and distorted point clouds. Second, we achieve accurate PCQA by integrating several conventional FR metrics and our SGW-based metrics using support vector regression. To our knowledge, this is the first study to introduce SGWs for PCQA. Experimental results demonstrate the proposed PCQA metric is more accurately correlated with subjective quality scores compared to conventional PCQA metrics.