Implementation of Grayscale Image Compression and Decoding Using Run-Length Encoding and Huffman Coding

The article describes how run-length encoding and Huffman coding can be implemented for grayscale image compression and decompression. This combined encoding approach can reduce image file sizes by up to 15 times compared to the original. The compression process typically involves two stages: first, run-length encoding identifies and compresses consecutive identical pixel values in the image matrix, effectively reducing spatial redundancy. This is particularly efficient for grayscale images with large uniform areas. The implementation would scan the image row by row, storing pixel values along with their repetition counts. Second, Huffman coding applies entropy encoding to the run-length compressed data, assigning shorter codes to more frequent symbols and longer codes to less common ones. This requires building a frequency table of the run-length symbols and constructing an optimal prefix code tree. The resulting bitstream represents the image data in a highly compact form. For decompression, the process reverses these steps: the Huffman-encoded data is first decoded using the same code tree, then run-length decoding reconstructs the original pixel sequence by expanding the count-value pairs. This lossless compression method ensures that the original image quality is perfectly restored without any detail loss during display or transmission. The implementation would typically involve functions for: - Image matrix preprocessing and serialization - Run-length encoding/decoding algorithms - Huffman tree construction and encoding/decoding routines - Bit-level I/O operations for efficient storage Thus, using run-length encoding combined with Huffman coding provides an effective and reliable method for image compression, offering significant storage savings while maintaining perfect reconstruction capability.