3D LiDAR point cloud image codec based on Tensor

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.

KEYWORDS:

• Light detection and ranging (LiDAR)
• point cloud compression
• statistical subspace outlier detection
• sparse optimization
• signal components
• signal block vectorization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes on contributors

Dr PL. Chithra, is a Professor in the Department of Computer Science, University of Madras. She graduated M.C.A and Ph.D. from Alagappa University and University of Madras, Tamil Nadu, India, respectively. Her research area of interest includes Digital Image Processing, Pattern Recognition and Artificial Neural Networks. She is currently working on 3D Medical Image segmentation, 3D Point Cloud Compression, Real time Image Processing techniques, Deep Learning and Network Security. She has conducted several refresher courses and published more than 82 papers in national and international journals. She has been serving as Organizing Chair and Program Chair of several National and International conferences.

A. Christoper Tamilmathi, is a Ph.D. Research scholar at Department of Computer Science, University of Madras. She received M.C.A., degree from Madurai Kamaraj University and M.Phil. from University of Madras, Tamil Nadu, India. She has qualified State Level Eligibility Test (SET) and National Eligibility Test (NET). Her research area includes Digital Image Processing, 3D Point Cloud Compression and Artificial Neural Networks.