An Enhanced Extended Huffman Coding Approach for Efficient Binary Data Compression

Abstract

Finding efficient and affordable ways to store and transmit digital data is becoming an urgent challenge. This research presents a method for lossless compression called Enhanced Extended Huffman Coding (EEHC). As opposed to the regular Binary Huffman Coding (BHC), which works with each symbol (the two values, 0 and 1), EEHC uses an algorithm that creates variable-size block based on the statistical frequency of data to create the associated multi-branch (base-8) Huffman tree that captures higher order (beyond the single symbols) statistical correlation between the data and, therefore, reduces the amount of redundant data. Comparative performance analysis indicates that the EEHC algorithm achieves substantial compression efficiency, yielding reductions of 77.49% (21,620 bytes from an initial 96,051-byte PDF file) and 86.00% (320,871 bytes from an original 2,291,925-byte MP4 file). These results demonstrate that EEHC attains a superior compression ratio relative to the conventional Binary Huffman Coding approach when applied to diverse digital media formats of comparable size. Additionally, the EEHC preserves lossless reconstruction; that is, it’s able to rebuild the original data exactly through the invertible multi-branch tree structure used for decompression. The results demonstrate that EEHC provides a good level of compression performance while maintaining the stability of the processing performance, and, therefore, makes the approach appropriate for many applications with large amounts of data.