Model-based encoding parameter optimization for 3D point cloud compression

Date

2018-11

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

Rate-distortion optimal 3D point cloud compression is very challenging due to the irregular structure of 3D point clouds. For a popular 3D point cloud codec that uses octrees for geometry compression and JPEG for color compression, we first find analytical models that describe the relationship between the encoding parameters and the bitrate and distortion, respectively. We then use our models to formulate the rate-distortion optimization problem as a constrained convex optimization problem and apply an interior point method to solve it. Experimental results for six 3D point clouds show that our technique gives similar results to exhaustive search at only about 1.57% of its computational cost.

Description

Keywords

Point cloud compression, rate-distortion optimization, rate and distortion models

Citation

Liu, Q., Yuan, H., Hou, J., Liu, H. and Hamzaoui, R. (2018) Model-based encoding parameter optimization for 3D point cloud compression. In: Proc. APSIPA ASC 2018, 10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Honolulu, Nov. 2018

Rights

Research Institute

Institute of Artificial Intelligence (IAI)
Institute of Engineering Sciences (IES)