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Rate Control for Geometry-Based LiDAR Point Cloud Compression via Multi-Factor Modeling
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DOI：10.1109/TBC.2024.3475808.png)
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Abstract
En 中文
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 l domain rate control algorithm for PT that unifies the various geometry influential factors in the expression of the minimum arc length dl to determine the final rate. A power-style geometry rate curve characterized by dl 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 rho 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 rho 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.
Keywords:
Rate control
geometry-based point cloud compression
LiDAR point cloud
bit allocation
rate control model parameter estimation
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