Data Visualization in static images is still dynamically growing and changing with time in recent days. In visualization applications, memory, time and bandwidth are crucial issues when handling the high resolution three dimensional (3D) Light Detection and Ranging (LiDAR) data and they progressively demand efficient data compression strategies. This shortage is strongly motivating us to develop an efficient 3D point cloud image compression methodology. This work introduces an innovative lossless compression algorithm for a 3D point cloud image based on higher-order singular value decomposition (HOSVD) technique. This algorithm starts with the preprocessing method which removes the unreliable 3D points and then it combines the HOSVD together with the normalization, predictive coding followed by Run Length encoding to compress the HOSVD coefficients. This work accomplished lower mean square error (MSE), high (infinitive) Peak signal noise ratio (PSNR) to produce the lossless decompressed 3D point cloud image. The storage size has been reduced to one by fourth of its original 3D LiDAR point cloud image size.
Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression
