Lossless Image Compression through Huffman Coding Technique and Its Application in Image Processing using MATLAB
Vikash Kumar1, Sanjay Sharma2
1Vikash Kumar, M.Tech Scholar, Department of Economic and Community, Millennium Institute of Technology & Science, Bhopal (M.P) India.
2Dr. Sanjay Sharma, Associate Professor, Department of Economic and Community, Millennium Institute of Technology & Science, Bhopal (M.P) India.
Manuscript received on February 22, 2017. | Revised Manuscript received on February 28, 2017. | Manuscript published on March 05, 2017. | PP: 10-13 | Volume-7 Issue-1, March 2017. | Retrieval Number: A2950037117/2017©BEIESP
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©The Authors. Published By: 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: Images include information about human body which is used for different purpose such as medical examination security and other plans Compression of images is used in some applications such as profiling information and transmission systems. Regard to importance of images information, lossless or loss compression is preferred. Lossless compressions are JPEG, JPEG-LS and JPEG2000 are few well-known methods for lossless compression. We will use differential pulse code modulation for image compression with Huffman encoder, which is one of the latest and provides good compression ratio, peak signal to noise ratio and minimum mean square error. . In this paper we try to answer the following question. Which entropy coding, Huffman, is more suitable compared to other from the compression ratio, performance, and implementation points of view? We have implemented and tested Huffman algorithms. Also we compare it with other existing methods with respect to parameter compression ratio, peak signal noise ratio.
Keywords: Lossless Compression, PSNR, Compression-Ratio, Encoding Technique, Huffman Coding, JPEG2000, JPEG-LS, JPEG