Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure

The dynamic point cloud is widely needed in 3D vision related applications such as virtual reality and telepresence. Due to the huge amount of data, a key technology before the effective application is the dynamic point cloud compression. The state-of-the-art dynamic point cloud compression scheme,...

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Main Authors: Haoyu Shi, Fan Li
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Open Journal of Signal Processing
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9737385/
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author Haoyu Shi
Fan Li
author_facet Haoyu Shi
Fan Li
author_sort Haoyu Shi
collection DOAJ
description The dynamic point cloud is widely needed in 3D vision related applications such as virtual reality and telepresence. Due to the huge amount of data, a key technology before the effective application is the dynamic point cloud compression. The state-of-the-art dynamic point cloud compression scheme, video-based point cloud compression (V-PCC), generates 2D videos with some uncorrelation due to the patch segmentation and packing process, which will affect the compression efficiency. In this paper, we propose a Packing with Patch Correlation Improvement (PPCI) algorithm to adaptively remove the uncorrelated parts between matched patches in packing for the sake of inter-prediction performance. We first propose a basic unidirectional patch re-segmentation operator to remove the uncorrelated parts of the patches in the current point cloud relative to the patches in its reference point cloud. The removed parts will be formed as new patches and added to the patch collection of the current point cloud. Then we propose a back-and-forth structure, which is a combination of several basic patch re-segmentation operators, to bilaterally remove the uncorrelated parts of matched patches in a back-and-forth (BF) unit. Furthermore, we propose a framework to adaptively decide the best length of each BF unit in a point cloud sequence. Experimental results show that our method achieves noticeable bitrate savings compared with the existing V-PCC packing methods, particularly for sequences with small motion.
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spelling doaj.art-64daa6dab6354708920c62489fdeb9d72022-12-22T02:22:24ZengIEEEIEEE Open Journal of Signal Processing2644-13222022-01-01315516810.1109/OJSP.2022.31603929737385Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth StructureHaoyu Shi0https://orcid.org/0000-0003-4857-2107Fan Li1https://orcid.org/0000-0002-7566-1634Shaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology, School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, ChinaShaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology, School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, ChinaThe dynamic point cloud is widely needed in 3D vision related applications such as virtual reality and telepresence. Due to the huge amount of data, a key technology before the effective application is the dynamic point cloud compression. The state-of-the-art dynamic point cloud compression scheme, video-based point cloud compression (V-PCC), generates 2D videos with some uncorrelation due to the patch segmentation and packing process, which will affect the compression efficiency. In this paper, we propose a Packing with Patch Correlation Improvement (PPCI) algorithm to adaptively remove the uncorrelated parts between matched patches in packing for the sake of inter-prediction performance. We first propose a basic unidirectional patch re-segmentation operator to remove the uncorrelated parts of the patches in the current point cloud relative to the patches in its reference point cloud. The removed parts will be formed as new patches and added to the patch collection of the current point cloud. Then we propose a back-and-forth structure, which is a combination of several basic patch re-segmentation operators, to bilaterally remove the uncorrelated parts of matched patches in a back-and-forth (BF) unit. Furthermore, we propose a framework to adaptively decide the best length of each BF unit in a point cloud sequence. Experimental results show that our method achieves noticeable bitrate savings compared with the existing V-PCC packing methods, particularly for sequences with small motion.https://ieeexplore.ieee.org/document/9737385/Dynamic point cloud compressionpackingpatch correlationvideo coding
spellingShingle Haoyu Shi
Fan Li
Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure
IEEE Open Journal of Signal Processing
Dynamic point cloud compression
packing
patch correlation
video coding
title Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure
title_full Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure
title_fullStr Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure
title_full_unstemmed Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure
title_short Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure
title_sort patch re segmentation and packing for dynamic point cloud compression via back and forth structure
topic Dynamic point cloud compression
packing
patch correlation
video coding
url https://ieeexplore.ieee.org/document/9737385/
work_keys_str_mv AT haoyushi patchresegmentationandpackingfordynamicpointcloudcompressionviabackandforthstructure
AT fanli patchresegmentationandpackingfordynamicpointcloudcompressionviabackandforthstructure