Joint Geometry and Color Projection-based Point Cloud Quality Metric

TL;DR

This paper introduces a novel point cloud quality metric that combines geometry and color projections to improve alignment accuracy, leveraging advanced 2D quality metrics for better correlation with human perception.

Contribution

It proposes a joint geometry and color projection-based metric that addresses misalignment issues and utilizes top-performing 2D metrics for enhanced quality assessment.

Findings

Achieves the best subjective-objective correlation among compared metrics.

Shows 17% and 14.2% Pearson correlation improvements over D1-PSNR and D2-PSNR.

Demonstrates robustness across various coding degradations.

Abstract

Point cloud coding solutions have been recently standardized to address the needs of multiple application scenarios. The design and assessment of point cloud coding methods require reliable objective quality metrics to evaluate the level of degradation introduced by compression or any other type of processing. Several point cloud objective quality metrics has been recently proposed to reliable estimate human perceived quality, including the so-called projection-based metrics. In this context, this paper proposes a joint geometry and color projection-based point cloud objective quality metric which solves the critical weakness of this type of quality metrics, i.e., the misalignment between the reference and degraded projected images. Moreover, the proposed point cloud quality metric exploits the best performing 2D quality metrics in the literature to assess the quality of the projected images. The experimental results show that the proposed projection-based quality metric offers the best subjective-objective correlation performance in comparison with other metrics in the literature. The Pearson correlation gains regarding D1-PSNR and D2-PSNR metrics are 17% and 14.2 when data with all coding degradations is considered.