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3D LiDAR point cloud image codec based on Tensor
The Imaging Science Journal ( IF 0.871 ) Pub Date : 2020-01-02 , DOI: 10.1080/13682199.2020.1719747 PL. Chithra 1 , A. Christoper Tamilmathi 1
The Imaging Science Journal ( IF 0.871 ) Pub Date : 2020-01-02 , DOI: 10.1080/13682199.2020.1719747 PL. Chithra 1 , A. Christoper Tamilmathi 1
Affiliation
ABSTRACT This paper proposes a new and efficient codec called 3D Light Detection and Ranging (LiDAR) point cloud coding based on tensor (LPCT) concepts. By combining the techniques of Statistical Subspace Outlier Detection and Logarithmic Transformation, LPCT effectively makes the unreliable points imperceptible and diminishes the spatial coefficient ranges. LPCT is applied to achieve the perfect encoding and decoding performances by using tensor. The iterative compression method is introduced to immensely reduce the dimension of a higher-order point cloud data. Experimental results reveal that the proposed LPCT yields a better independent compression ratio (CR) and impressive quality of a decompressed image than the existing well-liked compression approaches, namely 7-Zip and WinRAR. This work proves that the proposed lossless LPCT algorithm compresses the spatial information of various size point cloud images into six bytes and produces better Hausdorff peak signal-to-noise ratio (PSNR) for the shortest distance point cloud image.
中文翻译:
基于Tensor的3D LiDAR点云图像编解码
摘要本文提出了一种新的高效编解码器,称为基于张量 (LPCT) 概念的 3D 光检测和测距 (LiDAR) 点云编码。通过结合统计子空间离群点检测和对数变换技术,LPCT有效地使不可靠点变得不可察觉,并减小了空间系数范围。应用LPCT通过使用张量实现完美的编码和解码性能。引入迭代压缩方法以极大地降低高阶点云数据的维数。实验结果表明,与现有的广受欢迎的压缩方法(即 7-Zip 和 WinRAR)相比,所提出的 LPCT 产生了更好的独立压缩比 (CR) 和令人印象深刻的解压缩图像质量。
更新日期:2020-01-02
中文翻译:
基于Tensor的3D LiDAR点云图像编解码
摘要本文提出了一种新的高效编解码器,称为基于张量 (LPCT) 概念的 3D 光检测和测距 (LiDAR) 点云编码。通过结合统计子空间离群点检测和对数变换技术,LPCT有效地使不可靠点变得不可察觉,并减小了空间系数范围。应用LPCT通过使用张量实现完美的编码和解码性能。引入迭代压缩方法以极大地降低高阶点云数据的维数。实验结果表明,与现有的广受欢迎的压缩方法(即 7-Zip 和 WinRAR)相比,所提出的 LPCT 产生了更好的独立压缩比 (CR) 和令人印象深刻的解压缩图像质量。