Image and Text Compression Using Dynamic Huffman and RLE Coding

This Paper considers the design of lossless image and data compression methods dedicated to still images and text data. For Images, after a preprocessing step (RGB to gray transformation, resizing) and for text after a preprocessing step (ASCII conversion), dynamic Huffman and Run Length Encoding (RLE) is applied. The dynamic Huffman coding involves computing an approximation to the probabilities of occurrence "on the fly", as the ensemble is being transmitted with the aim to obtain the best possible compression ratio CR and Time Elapsed to compress. The additional parameters of evaluation in case of images are PSNR and MSE. The efficiency of the proposed methods is verified by applying these techniques to variety of data and images. Motivation behind this work is to provide a detail analysis of lossless compression methods which can be best suited in cognitive radio environment.