Efficient data compression techniques are increasingly necessary for processing LiDAR (Light Detection and Ranging) data. To address this need, a 3D point cloud data compression methodology based on Octree coding is presented in this paper. This codec involves quantizing the point clouds and compressing the 3D point cloud data using arithmetic encoding. Decompression is the reverse process used to reconstruct the original point cloud data. 3D LiDAR point cloud compression using Octree codec is a lossless technique that reduces the size of the data by identifying and removing less relevant data points while preserving the accuracy of the point cloud data. This approach enables easier storage and processing of the data, and the compression time for 3D point cloud data is calculated during the compression process. The proposed method is compared with the run-length compression method and Huffman coding method. It is found that the Octree codec with quantization and Arithmetic encoding outperforms existing Huffman coding and runlength coding