Development of Hybrid Compression Algorithm for Medical Images using Lempel-Ziv-Welch and Huffman Encoding
Ajala F. A.1, Adigun A.A2, Oke A.O3
1Ajala F. A., Department of Computer Science and Engineering, LAUTECH, Ogbomosho, Oyo State, Nigeria.
2Adigun A.A, Department of Computer Science and Engineering, LAUTECH, Ogbomosho, Oyo State, Nigeria.
3Oke A.O, Department of Computer Science and Engineering, LAUTECH, Ogbomosho, Oyo State, Nigeria.
Manuscript received on 05 November 2018 | Revised Manuscript received on 17 November 2018 | Manuscript published on 30 November 2018 | PP: 1-5 | Volume-7 Issue-4, November 2018 | Retrieval Number: D1774097418©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Image compression is of utmost importance in data processing, because of the cost savings it offers and because of the large volume of data transferred from one end to the other. The smaller the size of the data the better transmission speed and it also saves time. In communication, transmission of data efficiently, fast and noise free is essential.. Both the LZW and Huffman image compression algorithm are lossless in manner and these methods and some versions of them are very common in use of compressing images. On the average Huffman gives better compression results, while LZW give a better signal-noiseratio and when the compression efficiency gap between the LZW algorithm and its Huffman counterpart is the largest. In this work, Hybrid of LZW and Huffman image compression was developed and used. It gives better compression ratio and SNR than Huffman Encoding and LZW Algorithm. It also provides cheap, reliable and efficient system for image compression in digital communication system. The average result shows that Huffman encoding has 59.46% and LZW has1,99% of compression ratio, whereas the hybrid of Huffman and LZW has compression ratio of 47.61% but has 92.76% of Signal to Noise Ratio that produce better result of the original image.
Keywords: Compression, Transmission, Huffman, LZW, Encoding
Scope of the Article: Mobile App Design and Development