Most digital data are not stored in the most compact form. Rather, they are stored in whatever way makes them easiest to use, such as: ASCII text from word processors, binary code that can be executed on a computer, individual samples from a data acquisition system, etc. Typically, these easy-to-use encoding methods require data files about twice as large as actually needed to represent the information. Data compression is the general term for the various algorithms and programs developed to address this problem. A compression program is used to convert data from an easy-to-use format to one optimized for compactness. Likewise, an decompression program returns the information to its original form.
This research aims to appear the effect of a simple lossless compression method, RLE or Run Length Encoding , on another lossless compression algorithm which is the Huffman algorithm that generates an optimal prefix codes generated from a set of probabilities. While RLE simply replaces repeated bytes with a short description of which byte to repeat it.