IMAGE COMPRESSION USING DECISION TREE TECHNIQUE

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Published: Oct 30, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i8.4812
Keywords:
Lossless compression, Lossy compression, Peak Signal to Noise Ratio, Compression Ratio, Mean Square Error

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AMANPREET KAUR
Yadavindra College of Engineering, Talwandi Sabo
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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Vol. 8 No. 8 (2017): September-October
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