Arithmetic coding is a lossless compression algorithm with variable-length source coding. It is more flexible and efficient than well-known Huffman coding. In this paper we present a non-adaptive FPGA implementation of an arithmetic coding with separated statistical model of the data source. The alphabet of the data source is 256-symbol ASCII character set and does not include special end-of-file symbol. We have synthesized the design for Xilinx FPGA devices and used their built-in hardware multipliers.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 25283111Light detection and ranging (LiDAR) has the capability of capturing a huge among of highly accurate spatial data. However, the size of the data that causes a lot of problems associated with its exchange and storage. In this paper, a method for lossless LiDAR data compression is presented. Although efficient methods in terms of compression ratio have already been proposed, their time efficiency can be improved. For this purpose, a multithreading schema was developed to increase the use of the computer resources (i.e., multi-core central processor unit and direct memory access). In this way, the overall compression time has been reduced over 70%.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 15468310Contemporary Airborne Light Detection and Ranging (LiDAR) systems are capable to rapidly gather the data from large geographical areas with high precision and great density. As a result, obtained datasets can contain several tens of millions of points, making LiDAR data compression an important issue. In this paper, three domain-specific compression algorithms are compared against a general-purpose algorithm. Selected testing LiDAR datasets are derived from the practice to challenge common data compression issues. In this way, influences of the terrain type, point density, and number of contained points on the compression efficiency are studied.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 15285014