Compression time variable information using Huffman code

In this paper, we fit a function on probability density curve representing an information stream using artificial neural network. This methodology results in a specific function which represents a memorize able probability density curve. We then use the obtained function for information compression by Huffman algorithm. The difference between the proposed method with the general methods is, to use the Huffman algorithm in several times. In every time, the probability density function is fitted, estimated and then the information representing the function is added to end of the information stream. We next propose two different algorithms for information encoding and decoding using time variable estimation of probability density function. In order to evaluate the proposed algorithm, the percentage of the compression resulting our method has been compared with two popular methods named FDR code and Golomb at the end.