Full reference point cloud quality assessment using support vector regression

TL;DR

This paper introduces a fast and accurate full-reference point cloud quality assessment method using support vector regression, effectively handling various distortions with simple metrics and outperforming existing methods in accuracy and speed.

Contribution

The paper proposes a novel FR-PCQA method combining five simple metrics with SVR, achieving high accuracy and efficiency across multiple distortion types.

Findings

Outperforms conventional FR-PCQA methods in accuracy.

Faster than state-of-the-art methods using complex features.

Provides a practical, reliable benchmark for point cloud compression quality.

Abstract

Point clouds are a general format for representing realistic 3D objects in diverse 3D applications. Since point clouds have large data sizes, developing efficient point cloud compression methods is crucial. However, excessive compression leads to various distortions, which deteriorates the point cloud quality perceived by end users. Thus, establishing reliable point cloud quality assessment (PCQA) methods is essential as a benchmark to develop efficient compression methods. This paper presents an accurate full-reference point cloud quality assessment (FR-PCQA) method called full-reference quality assessment using support vector regression (FRSVR) for various types of degradations such as compression distortion, Gaussian noise, and down-sampling. The proposed method demonstrates accurate PCQA by integrating five FR-based metrics covering various types of errors (e.g., considering geometric distortion, color distortion, and point count) using support vector regression (SVR). Moreover, the proposed method achieves a superior trade-off between accuracy and calculation speed because it includes only the calculation of these five simple metrics and SVR, which can perform fast prediction. Experimental results with three types of open datasets show that the proposed method is more accurate than conventional FR-PCQA methods. In addition, the proposed method is faster than state-of-the-art methods that utilize complicated features such as curvature and multi-scale features. Thus, the proposed method provides excellent performance in terms of the accuracy of PCQA and processing speed. Our method is available from https://github.com/STAC-USC/FRSVR-PCQA.