3D Point Cloud Attribute Compression via Graph Prediction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)176-180
Journal / PublicationIEEE Signal Processing Letters
Volume27
Online published3 Jan 2020
Publication statusPublished - 2020

Abstract

3D point clouds associated with attributes are considered as a promising data representation for immersive communication. The large amount of data, however, poses great challenges to the subsequent transmission and storage processes. In this letter, we propose a new compression scheme for the color attribute of static voxelized 3D point clouds. Specifically, we first partition the colors of a 3D point cloud into clusters by applying k-d tree to the geometry information, which are then successively encoded. To eliminate the redundancy, we propose a novel prediction module, namely graph prediction, in which a small number of representative points selected from previously encoded clusters are used to predict the points to be encoded by exploring the underlying graph structure constructed from the geometry information. Furthermore, the prediction residuals are transformed with the graph transform, and the resulting transform coefficients are finally uniformly quantified and entropy encoded. Experimental results show that the proposed compression scheme is able to achieve better rate-distortion performance at a lower computational cost when compared with state-of-the-art methods.

Research Area(s)

  • 3D point cloud, compression, prediction, graph structure

Citation Format(s)

3D Point Cloud Attribute Compression via Graph Prediction. / Gu, Shuai; Hou, Junhui; Zeng, Huanqiang; Yuan, Hui.

In: IEEE Signal Processing Letters, Vol. 27, 2020, p. 176-180.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal