Benchmarking of objective quality metrics for point cloud compression

Abstract

Point cloud is a promising imaging modality for the representation of 3D media. The vast volume of data associated with it requires efficient compression solutions, with lossy algorithms leading to larger bit-rate savings at the expense of visual impairments. While conventional encoding approaches rely on efficient data structures, recent methods have incorporated deep learning for rate-distortion optimization, while inducing perceptual degradations of different natures. To measure the magnitude of such distortions, subjective or objective quality evaluation methodologies are employed. Lately, a remarkable amount of efforts has been devoted to the development of point cloud objective quality metrics, which have been reported to attain high prediction accuracy. However, their performance and generalization capabilities haven't been evaluated yet in presence of artifacts from learning-based codecs.