Fast Recolor Prediction Scheme in Point Cloud Attribute Compression

Abstract

Due to the emerging requirement of point cloud applications, efficient point cloud compression methods are in high demand for compact point cloud representation in limited bandwidth transmission. The compression standard GPCC (Geometry-based Point Cloud Compression) is led by the MPEG (Moving Picture Expert Group) in respond to industrial requirements. KNN (K-Nearest Neighbors) search based prediction method is adopted for point cloud attribute compression in current G-PCC, which only exploits Euclidean distance-based geometric relationship without fully consideration of underlying geometric distribution. In this paper, we propose a novel prediction scheme based on fast recolor technique for attribute lossless and near-lossless compression. Our method has been implemented upon G-PCC reference software of the latest version. Experimental results show that our method can take advantage of the correlation between the attributes of neighbors, which leads to better rate-distortion (R-D) performance than G-PCC anchor on point cloud dataset with negligible encode and decode time increase under the common test conditions.