Enhancing Data Compression: A Dynamic Programming Approach with Huffman Coding and Burrows-Wheeler Transform
Sairudra More, Vrinda Sanivarapu, Yukta Sharma, Ved Milind Thigale, M Suguna · 2023
Data compression is a vital method for reducing the size of digital data to save space and make transmitting it quicker. More advanced compression techniques, such as Huffman coding and Burrows-Wheeler Transform (BWT) algorithms, have been developed to achieve highly efficient compression. This study introduces a new data compression method that combines both Huffman coding and Burrows-Wheeler Transform (BWT) to compress images and demonstrates that this combined approach produces better results than using them separately. The BWT rearranges the order of characters in data to make it more suitable for compression, while Huffman coding assigns shorter codes to frequently occurring characters to make compression efficient. The code reads image data in portions, applies these transformations, and calculates compression statistics to show how effectively the compression is performed. Moreover, dynamic programming techniques are utilized to optimize the algorithm and enhance its efficiency. The study presents experimental findings that evaluate the performance of the proposed algorithm across various image types. The outcomes reveal that the proposed algorithm achieves higher compression ratios compared to conventional methods. Furthermore, the algorithm is efficient in both encoding and decoding processes, rendering it suitable for real-world applications. In conclusion, the proposed data compression algorithm that combines Huffman coding and the BWT algorithm is a successful strategy for compressing digital images. The integration of dynamic programming techniques has significantly elevated the algorithm's efficiency. This algorithm holds potential in diverse applications, including storing and transmitting large images.