Single and Binary Performance Comparison of Data Compression Algorithms for Text Files

Abstract

Data compression is a technique used to reduce the size of a file. To reduce the size of a file, unnecessary information is removed or parts that repeat the same information are stored once. Thus a lossless compression is achieved. The extracted file has all the features of the compressed original file and can be used in the same way. Data compression can be done using different techniques. Some of these techniques are Huffman coding, Lempel-Ziv-Welch coding and Burrows-Wheeler Transform. Techniques such as Huffman coding, Lempel-Ziv-Welch coding and Burrows-Wheeler Transform are some of them. Which technique to use depends on the type and size of the data to be compressed. Huffman, Lempel-Ziv-Welch, Burrows-Wheeler Transform and Deflate algorithms are the most widely used techniques for text compression. Each algorithm uses different approaches and can produce different results in terms of compression ratios and performance. In this study, different data compression techniques were measured on specific data sets by using them individually and in pairs on top of each other. The most successful result was obtained with the Deflate algorithm when used alone and the achieved compression ratio was 29.08. When considered in the form of stacked pairs, the compression ratio of the Burrows-Wheeler Transform and Deflate gave the best result as 57.36. In addition, when compression is performed in pairs, which algorithm is applied first and which algorithm is applied afterwards can make a significant difference in the compression ratio. In this study, the performance measurements obtained by applying the algorithms in different orders are compared and suggestions are presented to obtain optimum performance