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Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images
This paper introduces a novel deep learned quantization-based coding for 3D Airborne LiDAR (Light detection and ranging) point cloud (pcd) image (DLQCPCD). The raw pcd signals are sampled and transformed by applying the Nyquist signal sampling and Min-max signal transformation techniques, respective...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155491/ https://www.ncbi.nlm.nih.gov/pubmed/34055900 http://dx.doi.org/10.3389/frobt.2021.606770 |
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author | Tamilmathi, A. Christoper Chithra, P. L. |
author_facet | Tamilmathi, A. Christoper Chithra, P. L. |
author_sort | Tamilmathi, A. Christoper |
collection | PubMed |
description | This paper introduces a novel deep learned quantization-based coding for 3D Airborne LiDAR (Light detection and ranging) point cloud (pcd) image (DLQCPCD). The raw pcd signals are sampled and transformed by applying the Nyquist signal sampling and Min-max signal transformation techniques, respectively for improving the efficiency of the training process. Then, the transformed signals are feed into the deep learned quantization module for compressing the data. To the best of our knowledge, this proposed DLQCPCD is the first deep learning-based model for 3D airborne LiDAR pcd compression. The functions of Mean Squared Error and Stochastic Gradient Descent optimization function enhance the quality of the decompressed image by 67.01 percent on average, compared to other functions. The model’s efficiency has been validated with established well-known compression techniques such as the 7-Zip, WinRAR, and tensor tucker decomposition algorithm on the three inconsistent airborne datasets. The experimental results show that the proposed model compresses every pcd image into constant 16 Number of Neurons of data and decompresses the image with approximately 160 dB of PSNR value, 174.46 s execution time with 0.6 s execution speed per instruction, and proved that it outperforms the other existing algorithms regarding space and time. |
format | Online Article Text |
id | pubmed-8155491 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81554912021-05-28 Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images Tamilmathi, A. Christoper Chithra, P. L. Front Robot AI Robotics and AI This paper introduces a novel deep learned quantization-based coding for 3D Airborne LiDAR (Light detection and ranging) point cloud (pcd) image (DLQCPCD). The raw pcd signals are sampled and transformed by applying the Nyquist signal sampling and Min-max signal transformation techniques, respectively for improving the efficiency of the training process. Then, the transformed signals are feed into the deep learned quantization module for compressing the data. To the best of our knowledge, this proposed DLQCPCD is the first deep learning-based model for 3D airborne LiDAR pcd compression. The functions of Mean Squared Error and Stochastic Gradient Descent optimization function enhance the quality of the decompressed image by 67.01 percent on average, compared to other functions. The model’s efficiency has been validated with established well-known compression techniques such as the 7-Zip, WinRAR, and tensor tucker decomposition algorithm on the three inconsistent airborne datasets. The experimental results show that the proposed model compresses every pcd image into constant 16 Number of Neurons of data and decompresses the image with approximately 160 dB of PSNR value, 174.46 s execution time with 0.6 s execution speed per instruction, and proved that it outperforms the other existing algorithms regarding space and time. Frontiers Media S.A. 2021-05-13 /pmc/articles/PMC8155491/ /pubmed/34055900 http://dx.doi.org/10.3389/frobt.2021.606770 Text en Copyright © 2021 Tamilmathi and Chithra. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Tamilmathi, A. Christoper Chithra, P. L. Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images |
title | Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images |
title_full | Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images |
title_fullStr | Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images |
title_full_unstemmed | Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images |
title_short | Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images |
title_sort | deep learned quantization-based codec for 3d airborne lidar point cloud images |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155491/ https://www.ncbi.nlm.nih.gov/pubmed/34055900 http://dx.doi.org/10.3389/frobt.2021.606770 |
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