Bounded-Error LiDAR Compression for Bandwidth-Efficient Cloud-Edge In-Vehicle Data Transmission
Abstract
:1. Introduction
2. Related Works
3. Proposed Bounded-Error Compression Methods
3.1. Bounded-Error Quantization and Error Metrics
3.2. Huffman Coding (Lossless Baseline)
3.3. EB-HC (Axis/L2)
Algorithm 1. EB-HC |
Require: A set of LiDAR points , user-specified threshold δ > 0, and mode ∈ {Axis, L2} |
Ensure: A compressed bitstream |
|
3.4. EB-3D (Axis/L2)
Algorithm 2. EB-3D |
Require: A set of LiDAR points ; threshold ; mode |
Ensure: An octree-based compressed representation |
|
3.5. EB-HC-3D (Axis/L2)
Algorithm 3. EB-HC-3D |
Require: A set of LiDAR points P; threshold ; mode ∈ {Axis, L2}; maximum depth Ensure: A final compressed bitstream
|
3.6. Summary of Proposed Methods
4. Evaluation
4.1. Dataset Introduction
4.2. Evaluation Metrics and Error Settings
4.3. Comparison Among Compression Methods
4.4. Cloud-Edge Application Analysis
5. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
ADASs | Advanced Driver-Assistance Systems |
ECU | Electronic Control Unit |
EB-HC | Error-Bounded Huffman Coding |
EB-3D | Error-Bounded 3D Compression Method |
EB-HC-3D | Error-Bounded Huffman Coding with 3D Integration |
LiDAR | Light Detection and Ranging |
T | Maximum Allowed Coordinate Deviation (Error Threshold) |
V2X | Vehicle-to-Everything |
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Method | Key Idea | Time Complexity |
---|---|---|
Huffman | Frequency-based byte encoding (baseline) | |
EB-HC (Axis) | Merge integer coords along x, y, z + Huffman | |
EB-HC (L2) | Merge integer coords in 3D distance + Huffman | |
EB-3D (Axis) | Octree subdivision (per dimension) | |
EB-3D (L2) | Octree subdivision (radial check) | |
EB-HC-3D (Axis) | Combine octree + Huffman (axis-based) | |
EB-HC-3D (L2) | Combine octree + Huffman (L2-based) |
Symbol | Definition |
---|---|
CR | Compression ratio, defined as the ratio of the number of bits in the compressed bitstream to that of the raw quantized data. |
Encoding time, measured in seconds. This is the time required to compress a point cloud. | |
Decoding time, measured in seconds. This is the time required to decompress the data. | |
Axis-wise error, defined as the maximum absolute deviation along any coordinate between an original point and its reconstruction . | |
Euclidean (L2) error, defined as the Euclidean distance between the original point and its reconstruction . | |
CD | A local geometric completeness metric: A lower CD indicates fewer small-scale errors. |
Occupancy IoU | A global volumetric completeness metric: . A higher IoU indicates better overall coverage |
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Chang, R.-I.; Hsu, T.-W.; Yang, C.; Chen, Y.-T. Bounded-Error LiDAR Compression for Bandwidth-Efficient Cloud-Edge In-Vehicle Data Transmission. Electronics 2025, 14, 908. https://doi.org/10.3390/electronics14050908
Chang R-I, Hsu T-W, Yang C, Chen Y-T. Bounded-Error LiDAR Compression for Bandwidth-Efficient Cloud-Edge In-Vehicle Data Transmission. Electronics. 2025; 14(5):908. https://doi.org/10.3390/electronics14050908
Chicago/Turabian StyleChang, Ray-I, Ting-Wei Hsu, Chih Yang, and Yen-Ting Chen. 2025. "Bounded-Error LiDAR Compression for Bandwidth-Efficient Cloud-Edge In-Vehicle Data Transmission" Electronics 14, no. 5: 908. https://doi.org/10.3390/electronics14050908
APA StyleChang, R.-I., Hsu, T.-W., Yang, C., & Chen, Y.-T. (2025). Bounded-Error LiDAR Compression for Bandwidth-Efficient Cloud-Edge In-Vehicle Data Transmission. Electronics, 14(5), 908. https://doi.org/10.3390/electronics14050908