Improving PSNR-based Quality Metrics Performance For Point Cloud Geometry

TL;DR

This paper introduces improved PSNR-based quality metrics for point cloud geometry, leveraging intrinsic PC characteristics and rendering processes, resulting in significantly better correlation with perceived quality.

Contribution

The paper proposes novel PSNR-based metrics tailored for point cloud geometry, enhancing accuracy over existing methods by exploiting intrinsic PC features and rendering effects.

Findings

Best metrics outperform state-of-the-art by up to 32% in Pearson correlation

Improved metrics better capture perceived point cloud quality

Experimental validation confirms enhanced reliability of proposed metrics

Abstract

An increased interest in immersive applications has drawn attention to emerging 3D imaging representation formats, notably light fields and point clouds (PCs). Nowadays, PCs are one of the most popular 3D media formats, due to recent developments in PC acquisition, namely with new depth sensors and signal processing algorithms. To obtain high fidelity 3D representations of visual scenes a huge amount of PC data is typically acquired, which demands efficient compression solutions. As in 2D media formats, the final perceived PC quality plays an important role in the overall user experience and, thus, objective metrics capable to measure the PC quality in a reliable way are essential. In this context, this paper proposes and evaluates a set of objective quality metrics for the geometry component of PC data, which plays a very important role in the final perceived quality. Based on the popular PSNR PC geometry quality metric, the novel improved PSNR-based metrics are proposed by exploiting the intrinsic PC characteristics and the rendering process that must occur before visualization. The experimental results show the superiority of the best-proposed metrics over the state-of-the-art, obtaining an improvement of up to 32% in the Pearson correlation coefficient.