Implementation of Huffman Coding for Digital Image Compression

In this document, we discuss the application of Huffman coding, particularly its role in digital image compression and decompression. Huffman coding serves as a critical step in digital image compression by effectively reducing the storage space required for image data. The implementation typically involves analyzing pixel frequency distributions, constructing an optimal prefix code tree, and encoding the image data using variable-length codes based on frequency probabilities. Besides the encoding process, the decoding step is equally essential for digital image decompression. The decoding algorithm reconstructs the compressed image data by traversing the Huffman tree using the encoded bitstream, ultimately restoring the image to its original state. Understanding and mastering Huffman coding implementation, including key functions like probability calculation, tree building, and bitstream processing, proves highly beneficial for efficient digital image compression and decompression workflows.