A biometrie iris image compression using LZW and hybrid LZW coding algorithm

Image Compression is used to decrease the number of bits required to store and transmit images without any measurable loss of information. The impact of using different lossless compression algorithms on the compression ratios and timings when processing various biometric sample data is investigated. The huge challenge in using biometrics data is about handling of enormous data. As this data is increasing day by day, it becomes difficult to store and transmit the data effectively and efficiently in less time. To solve these kind of problem, in this papers presents a Lempel-Ziv-Welch (LZW) Compression and Hybrid LZW Compression algorithms of image processing in accounts using compression techniques that are in use in Biometric eye iris images. A Three-stage structure is embedded in this scheme. A Image pre-processing is used to de-correlate the raw image data in the first stage. Then in the second stage, a Feature extraction scheme based on the patch based entropy prediction. This newly proposed scheme could reduce the cost for the Hybrid LZW coding while achieving high compression ratio. The experimental work is done in MATLAB simulation using compression method. We can achieve compressed images with better compression ratio than existing methods of Huffman and LZW Compression algorithms.