Rate Control for Geometry-Based LiDAR Point Cloud Compression via Multi-Factor Modeling

Geometry-based Point Cloud Compression (G-PCC) standard developed by the Moving Picture Experts Group has shown a promising prospect for compressing extremely sparse point clouds captured by the Light Detection And Ranging (LiDAR) equipment. However, as an essential functionality for low delay and limited bandwidth transmission, rate control for Geometry-based LiDAR Point Cloud Compression (G-LPCC) has not been fully studied. In this paper, we propose a rate control scheme for G-LPCC. We first adopt the best configuration of G-PCC for the LiDAR point cloud as the basis in terms of the Rate-Distortion (R-D) performance, which is the predictive tree (PT) for geometry compression and Region Adaptive Haar Transform (RAHT) for attribute compression. The common challenge of designing rate control algorithms for PT and RAHT is that their rates are determined by multiple factors. To address that, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> domain rate control algorithm for PT that unifies the various geometry influential factors in the expression of the minimum arc length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {d}l$ </tex-math></inline-formula> to determine the final rate. A power-style geometry rate curve characterized by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {d}l$ </tex-math></inline-formula> has been modeled. By analyzing the distortion behavior of different quantization parameters, an adaptive bitrate control method is proposed to improve the R-D performance. In addition, we borrow the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> factor from the previous 2D video rate control and successfully apply it to RAHT rate control. A simple and clean linear attribute rate curve characterized by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> has been modeled, and a corresponding parameter estimation method based on the cumulative distribution function is proposed for bitrate control. The experimental results demonstrate that the proposed rate control algorithm can achieve accurate rate control with additional Bjontegaard-Delta-rate (BD-rate) gains.

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Publication Details

Journal
IEEE Transactions on Broadcasting
Published
2024-10-25
DOI
https://doi.org/10.1109/tbc.2024.3475808
Citations
2
Primary Topic
Advanced Vision and Imaging
Type
article
Field-Weighted Citation Impact
0.50

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article

Rate Control for Geometry-Based LiDAR Point Cloud Compression via Multi-Factor Modeling

Lizhi Hou, Yiling Xu, Linyao Gao, Jenq-Neng Hwang et al.
2 citations
IEEE Transactions on Broadcasting
Advanced Vision and Imaging
0.50
article

Rate Control for Geometry-Based LiDAR Point Cloud Compression via Multi-Factor Modeling

Lizhi Hou, Yiling Xu, Linyao Gao, Jenq-Neng Hwang, Dong Wang, Qian Zhang
article en
2 citations

Abstract

Geometry-based Point Cloud Compression (G-PCC) standard developed by the Moving Picture Experts Group has shown a promising prospect for compressing extremely sparse point clouds captured by the Light Detection And Ranging (LiDAR) equipment. However, as an essential functionality for low delay and limited bandwidth transmission, rate control for Geometry-based LiDAR Point Cloud Compression (G-LPCC) has not been fully studied. In this paper, we propose a rate control scheme for G-LPCC. We first adopt the best configuration of G-PCC for the LiDAR point cloud as the basis in terms of the Rate-Distortion (R-D) performance, which is the predictive tree (PT) for geometry compression and Region Adaptive Haar Transform (RAHT) for attribute compression. The common challenge of designing rate control algorithms for PT and RAHT is that their rates are determined by multiple factors. To address that, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> domain rate control algorithm for PT that unifies the various geometry influential factors in the expression of the minimum arc length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {d}l$ </tex-math></inline-formula> to determine the final rate. A power-style geometry rate curve characterized by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {d}l$ </tex-math></inline-formula> has been modeled. By analyzing the distortion behavior of different quantization parameters, an adaptive bitrate control method is proposed to improve the R-D performance. In addition, we borrow the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> factor from the previous 2D video rate control and successfully apply it to RAHT rate control. A simple and clean linear attribute rate curve characterized by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> has been modeled, and a corresponding parameter estimation method based on the cumulative distribution function is proposed for bitrate control. The experimental results demonstrate that the proposed rate control algorithm can achieve accurate rate control with additional Bjontegaard-Delta-rate (BD-rate) gains.

IEEE Transactions on BroadcastingVol. 71(1)
Shanghai Jiao Tong University (CN), University of Washington (US), Ambulance Care (Sweden) (SE)
National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 34%
Advanced Vision and Imaging
0.50
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