Enhancing Data Compression Techniques for Optimization: A Novel Integration of Burrows-Wheeler Transform, Lempel-Ziv-Welch, Run-Length Encoding and Huffman Coding

This research paper introduces two innovative algorithms combining Burrows-Wheeler Transform (BWT) with Lempel-Ziv-Welch (LZW) and Run-Length Encoding (RLE), integrated with Huffman coding. These algorithms aim to enhance compression efficiency by combining BWT with RLE and LZW, exploiting their unique characteristics. Run-Length Encoding simplifies repetitive pattern representation, LZW offers dynamic dictionary-based compression, and Huffman coding optimizes code lengths based on symbol frequencies. The algorithms are evaluated across various datasets to assess compression performance, including compression ratios and encoding/decoding speeds. Experimental results demonstrate the superiority of BWT+HC+RLE and BWT+LZW+HC algorithms over traditional methods, showing promising applications in domains requiring efficient compression. The integration of diverse techniques, along with Huffman coding, presents a comprehensive approach adaptable to various real-world applications.