A novel methodology for quality assessment of voxelized point clouds

Recent trends in multimedia technologies indicate a significant growth of interest for new imaging modalities that aim to provide immersive experiences by increasing the engagement of the user with the content. Among other solutions, point clouds denote an alternative 3D content representation that allows visualization of static or dynamic scenes in a more immersive way. As in many imaging applications, the visual quality of a point cloud content is of crucial importance, as it directly affects the user experience. Despite the recent efforts from the scientific community, subjective and objective quality assessment for this type of visual data representation remains an open problem. In this paper, we propose a new, alternative framework for quality assessment of point clouds. In particular, we develop a rendering software, which performs real-time voxelization and projection of the 3D point clouds onto 2D planes, while allowing interaction between the user and the projected views. These projected images are then employed by two-dimensional objective quality metrics, in order to predict the perceptual quality of the displayed stimuli. Benchmarking results, using subjective ratings that were obtained through experiments in two test laboratories, show that our framework provides high predictive power and outperforms the state of the art in objective quality assessment of point cloud imaging.