On the Performance of Temporal Pooling Methods for Quality Assessment of Dynamic Point Clouds
Pedro Garcia Freitas, Mateus Gonçalves, Johann Homonnai, Rafael Diniz, Mylène C. Q. Farias · 2022
Point Clouds (PCs) are collections of points distributed in the 3D space, containing attributes such as color, normals, transparency, and specularity. Dynamic Point Clouds (DPCs) correspond to sequences of points in the 3D space that vary over time like pixels vary over time in a conventional video. Dynamic PCs are a suitable way to represent volumetric videos that can be used in augmented or virtual reality applications. This representation, however, requires a large number of points to achieve a high quality of experience and needs to be compressed before storage and transmission. Therefore, reliable quality metrics are needed in order to automatically estimate the perceptual quality of dynamic PC contents. Since currently there are several quality assessment metrics for static PC, a possible approach solution consists of using temporal pooling functions to combine the quality scores predicted for each of the frames. In this paper, we study the effects of different temporal pooling strategies on the performance of dynamic PC quality assessment methods. Our experimental tests were performed using a recent publicly-available database, demonstrating the efficiency of the evaluated temporal pooling models. More specifically, the work provides a recipe on how to apply a temporal pooling function to combine frame-based quality predictions generated with texture-based static PC quality assessment methods to estimate the quality of dynamic PCs.