3D point cloud attribute compression using geometry-guided sparse representation

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...

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Main Authors: Gu, Shuai, Hou, Junhui, Zeng, Huanqiang, Yuan, Hui, Ma, Kai-Kuang
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/154489
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1544892021-12-23T07:13:44Z 3D point cloud attribute compression using geometry-guided sparse representation Gu, Shuai Hou, Junhui Zeng, Huanqiang Yuan, Hui Ma, Kai-Kuang School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering 3D Point Cloud Sparse Representation 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 ℓ0-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. This work was supported in part by the National Natural Science Foundation of China under Grant 61871434, Grant 61871342, and Grant 61571274, in part by the Natural Science Foundation for Outstanding Young Scholars of Fujian Province under Grant 2019J06017, in part by the Hong Kong RGC Early Career Scheme Funds under Grant 9048123, in part by the Shandong Provincial Key Research and Development Plan under Grant 2017CXGC150, in part by the Fujian-100 Talented People Program, in part by the High-level Talent Innovation Program of Quanzhou City under Grant 2017G027, in part by the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University under Grant ZQN-YX403, and in part by the High-Level Talent Project Foundation of Huaqiao University under Grant 14BS201 and Grant14BS204. Part of this article was presented at the IEEE ICASSP2017 2021-12-23T07:13:44Z 2021-12-23T07:13:44Z 2019 Journal Article Gu, S., Hou, J., Zeng, H., Yuan, H. & Ma, K. (2019). 3D point cloud attribute compression using geometry-guided sparse representation. IEEE Transactions On Image Processing, 29, 796-808. https://dx.doi.org/10.1109/TIP.2019.2936738 1057-7149 https://hdl.handle.net/10356/154489 10.1109/TIP.2019.2936738 31478850 2-s2.0-85073537536 29 796 808 en IEEE Transactions on Image Processing © 2019 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
3D Point Cloud
Sparse Representation
spellingShingle Engineering::Electrical and electronic engineering
3D Point Cloud
Sparse Representation
Gu, Shuai
Hou, Junhui
Zeng, Huanqiang
Yuan, Hui
Ma, Kai-Kuang
3D point cloud attribute compression using geometry-guided sparse representation
description 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 ℓ0-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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Gu, Shuai
Hou, Junhui
Zeng, Huanqiang
Yuan, Hui
Ma, Kai-Kuang
format Article
author Gu, Shuai
Hou, Junhui
Zeng, Huanqiang
Yuan, Hui
Ma, Kai-Kuang
author_sort Gu, Shuai
title 3D point cloud attribute compression using geometry-guided sparse representation
title_short 3D point cloud attribute compression using geometry-guided sparse representation
title_full 3D point cloud attribute compression using geometry-guided sparse representation
title_fullStr 3D point cloud attribute compression using geometry-guided sparse representation
title_full_unstemmed 3D point cloud attribute compression using geometry-guided sparse representation
title_sort 3d point cloud attribute compression using geometry-guided sparse representation
publishDate 2021
url https://hdl.handle.net/10356/154489
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