Analysis and comparison of adaptive huffman coding and arithmetic coding algorithms

One ongoing problem with digital image file sizes is the cost of transferring data over a network. Larger image file sizes result in larger webpage file sizes. Larger webpage file sizes take more time to load and users incur higher data transfer costs when opening these larger webpages. It is cheaper and faster to utilize smaller compressed files than to use larger uncompressed files. Compression usually reduces the size of files for improved storage and online transmission of the files. Some types of files will compress better than other types, for example certain text files and BMP image files may even be compressed by up to 90%. This paper is part of a research work carried out to analyze and compare the efficiency of Adaptive Huffman coding with Arithmetic coding compression algorithms using various image files to assess the efficiency of the two different compression methods and to assess possible combinations of the compared compression algorithms which effectively reduce the image file size with minimal time complexity overhead. The research design focused on experiments such that a set of image files were sampled and systematically compressed. After progressively and systematically compressing a subset of sample files with the two selected general compression methods; it was observed that a fast Arithmetic coding (AC) variant consistently yielded fast compression times and high space-savings (file size reduction) results for image files and audio files. Additionally, reducing a BMP image's range of bytes (number of different bytes within the data of the file), using vector quantization, would also increase the space-savings result of compressing that BMP file with the fast AC variant.