Huffman Coding Algorithm for Digital Image Compression and Decompression

In this documentation, we implement the Huffman coding algorithm for compressing and decompressing digital images. This algorithm enables us to reduce image file sizes while maintaining image quality through efficient entropy coding. The implementation involves constructing a Huffman tree based on the frequency distribution of pixel values in the source image, where more frequent pixels receive shorter binary codes. By applying Huffman coding, we significantly reduce storage requirements for image files. The decompression process reconstructs the image using the same Huffman tree, producing output that is nearly identical to the original source image. This approach allows substantial storage space savings without compromising image quality. The algorithm achieves infinite peak signal-to-noise ratio (PSNR) because Huffman coding is a lossless compression method. The decompression process perfectly reconstructs the original data without introducing any noise or distortion. Key implementation steps include frequency analysis, Huffman tree construction using priority queues, binary code assignment, and sequential bit-stream encoding/decoding. Therefore, utilizing Huffman coding for image compression and decompression provides an efficient and reliable method for digital image processing, particularly suitable for applications requiring exact data reconstruction.