Improving PSNR-based Quality Metrics Performance For Point Cloud Geometry
This work addresses the need for reliable objective quality metrics for point cloud geometry, which is crucial for immersive applications, but it is incremental as it builds on existing PSNR methods.
The paper tackled the problem of measuring point cloud geometry quality by proposing improved PSNR-based metrics, resulting in up to a 32% improvement in Pearson correlation coefficient compared to state-of-the-art methods.
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.