3D LiDAR point cloud image codec based on Tensor. (2nd January 2020)

• Record Type: Journal Article
• Authors: Chithra, PL.; Tamilmathi, A. Christoper
• Is Part Of: Imaging science journal. Volume 68:Number 1(2020)
• Page Start: 1
• Page End: 10
• Publication Date: 2020-01-02
• Subjects: Light detection and ranging (LiDAR) -- point cloud compression -- statistical subspace outlier detection -- sparse optimization -- signal components -- signal block vectorization; Imaging systems -- Periodicals; 621.36705
• DOI: 10.1080/13682199.2020.1719747
• Languages: English
• ISSNs: 1368-2199

Abstract

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.