IMAGE COMPRESSION USING DECISION TREE TECHNIQUE

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AMANPREET KAUR
Balkrishan Jindal

Abstract

Image compression is the process of minimizing the size of file without degrading the file quality. The reduction in file size permits number of files to be stored in a memory space. The proposed compression method used the decision tree algorithm for compression of a grey scale images. The Decision tree reads an image which needs to compress. Patches are created and assign weight to each patch on the basis of similarity between patches. Decision tree is formed based on the similarity between the pixels. Then data is merged to form compressed image otherwise define root node which has maximum weight, left node which has less weight than root node and right node which has minimum weight and decision tree is created. The proposed method is evaluated using the Image Quality Measures (IQM) like Peak-Signal to Noise Ratio (PSNR), Compression Ratio (CR), Mean Square Error (MSE) and Elapsed Time (ET). The experimental results of the proposed method are compared with Huffman method and Wavelet Difference Reduction method. From the experimental results it has been concluded that the proposed method is better than Huffman method and Wavelet Difference Reduction.

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References

Kaur, M. and Kaur N. 2015. A Literature Survey on Lossless Image Compression. International Journal of Advanced Research in Computer and Communication Engineering. 4(3): pp. 491-493.

Sharma, M. 2010. Compression Using Huffman Coding. International journal of computer science and network security. 10(5): pp.133-141.

Bansal, V., Gupta, P. and Tomar S. 2014. The Implementation of Run Length Encoding for RGB Image Compression. International Journal of Advanced Research in Computer Engineering and Technology. 3 (12): pp. 4397-4401.

Pujar, J. and Kadlaskar. 2010. A New Lossless Method of Image Compression and Decompression Using Huffman Coding Techniques. Journal of Theoretical and Applied Information Technology, pp.18-23.

Kaimal, A. and Manimurugan, S. 2013. Image Compression Techniques: A Survey International Journal of Engineering Inventions. 2 (4): pp. 26-28.

Sharma, S. August 2010. Digital Image Processing. Department of Electronics & Communication Engg.

Anitha, S. 2015. Lossless Image Compression And Decompression Using Huffman Coding. International Research Journal of Engineering and Technology, 3(1): pp. 240-247.

Benchikh, S and Corinthios, M. A Hybrid Image Compression Technique Based on DWT Transforms. Proc. of the IEEE Transactions on industrial electronics, June, Canada.

Islam, R., Baki, A. and Palash, S. 2009. A New Image Compression Scheme Using Repeat Reduction and Arithmetic Coding. International Conference on Computer and Information Technology, pp. 209-214.

Raja, S. and Suruliandi, A. 2011. Image Compression using WDR & ASWDR Techniques with different Wavelet Codecs. Proc. of Int. Conf. on Advances in Computer Engineering, pp. 102-105.

Grgic, S. and Grgic, M. 2001. Performance Analysis of Image Compression Using Wavelets. Proc. Of the IEEE Transactions on industrial electronics, June, Zovko-Cihlar, 682–695.

Geng, X. and Ding, Q. 2012. Similar-short periodicity analysis and application in image compression encryption of digital chaos. International Workshop on Chaos-fractals Theories and Applications.

Kau, L. and Lin , Y. 2007. Least-Squares-Based Switching Structure for Lossless Image Coding. Proc. of the IEEE transactions on circuits and system, vol. 54.pp. 1529-1541.

Jasmi, R., Permual, B., and Rajasekaran, P. 2015. Comparison of image compression techniques using Huffman coding, DWT and Fractal algorithms. International Conference on Computer Communication and Informatics (ICCCI), 08 -10 Jan. Coimbatore.

Parmer, H. 2014. Comparison of DCT and Wavelet based Image Compression Techniques. International Journal Engineering Development and Research. 2(1):pp.664-669.

Kaur, D. and Kaur, K. 2013. Huffman Based LZW Lossless Image Compression Using Retinex Algorithm. International Journal of Advance Research in Computer and Communication Engineering. 2(8): pp. 3145-3151.

Kaur, M. and Kaur, G. 2013. A Survey of Lossless and Lossy Compression Techniques. International Journal of Advance Research in Computer Science and Software Engineering. 3(2): pp. 323-326.