Paper Info
Title
3D Point Cloud Attribute Compression Using Geometry-Guided Sparse Representation.
Abstract
3D point clouds associated with attributes are considered as a promising paradigm for immersive communication. However, the corresponding compression schemes for this media are still in the infant stage. Moreover, in contrast to conventional image/video compression, it is a more challenging task to compress 3D point cloud data, arising from the irregular structure. In this paper, we propose a novel and effective compression scheme for the attributes of voxelized 3D point clouds. In the first stage, an input voxelized 3D point cloud is divided into blocks of equal size. Then, to deal with the irregular structure of 3D point clouds, a geometry-guided sparse representation (GSR) is proposed to eliminate the redundancy within each block, which is formulated as an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm regularized optimization problem. Also, an inter-block prediction scheme is applied to remove the redundancy between blocks. Finally, by quantitatively analyzing the characteristics of the resulting transform coefficients by GSR, an effective entropy coding strategy that is tailored to our GSR is developed to generate the bitstream. Experimental results over various benchmark datasets show that the proposed compression scheme is able to achieve better rate-distortion performance and visual quality, compared with state-of-the-art methods.
Year
DOI
Venue
2020
10.1109/TIP.2019.2936738
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Keywords
DocType
Volume
Three-dimensional displays,Image coding,Transforms,Image color analysis,Redundancy,Entropy coding
Journal
29
Issue
ISSN
Citations 
1
1057-7149
6
PageRank 
References 
Authors
0.47
10
5
Name
Order
Citations
PageRank
Shuai Gu160.47
Junhui Hou239549.84
Huanqiang Zeng339536.94
Hui Yuan4256.21
Kai-Kuang Ma52309180.29