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LPCM: Learning-Based Predictive Coding for LiDAR Point Cloud Compression
Summary
This study introduces a learning-based predictive coding method (LPCM) for efficient LiDAR point cloud compression. LPCM improves data compression by exploiting angular resolution and adapting to different bitrates, outperforming existing methods.
Area of Science:
- Computer Vision
- Data Compression
- Machine Learning
Background:
- LiDAR point clouds are essential for many applications but generate massive data volumes, necessitating efficient compression.
- Existing learning-based compression methods fail to leverage LiDAR's angular resolution and adapt to varying bitrate correlations.
- Current geometry-based point cloud compression (G-PCC) methods use limited linear prediction for angular features and lack optimized quantization for spherical coordinates.
Purpose of the Study:
- To develop a novel learning-based predictive coding method (LPCM) for enhanced LiDAR point cloud compression.
- To address limitations in existing methods by exploiting angular resolution and adapting to different bitrate correlations.
- To improve rate-distortion performance without increasing coding time.
Main Methods:
- LPCM converts point clouds into predictive trees using a spherical coordinate system, featuring distinct high-bitrate and low-bitrate coding modes.
- A Long-Short-Term Memory-based predictive (LSTM-P) module is employed in high-bitrate mode for long-term geometry correlation.
- A variational radius compression (VRC) module and a differential evolution (DE)-based quantization parameter selection method are used for low-bitrate mode and optimized quantization.
Main Results:
- LPCM achieved a 21.2% D1-PSNR BD-rate reduction compared to G-PCC lossless octree-based coding on SemanticKITTI.
- A 5.6% BD-rate reduction was observed compared to PredGeom on Ford dataset.
- The proposed DE-based quantization improved rate-distortion performance without additional coding time.
Conclusions:
- LPCM offers a significant improvement in LiDAR point cloud compression efficiency, particularly at different bitrates.
- The method effectively utilizes spherical coordinate properties and advanced machine learning modules for superior prediction and compression.
- LPCM represents a promising advancement in point cloud compression, outperforming current standards and methods.
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