Volume 9, Issue 2 (3-2013)
JSDP 2013, 9(2): 61-74 |
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Compression time variable information using Huffman code . JSDP 2013; 9 (2) :61-74
URL: http://jsdp.rcisp.ac.ir/article-1-144-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-144-en.html
Abstract: (13256 Views)
Abstract: In this paper, we fit a function on probability density curve representing an information stream using artificial neural network . This methodology result is a specific function which represent a memorize able probability density curve . we then use the resulting function for information compression by Huffman algorithm . the difference between the proposed me then with the general methods is , using 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 [4] and Golomb1 at the end
Keywords: variable time probability density function, neural network, compression information, memorize able information stream, Huffman code
Type of Study: Research |
Subject:
Paper
Received: 2013/07/7 | Accepted: 2013/08/25 | Published: 2013/08/25 | ePublished: 2013/08/25
Received: 2013/07/7 | Accepted: 2013/08/25 | Published: 2013/08/25 | ePublished: 2013/08/25
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