Image Compression Techniques Comparative Analysis using SVD-WDR and SVD-WDR with Principal Component Analysis
Main Article Content
The image processing is the technique which can process the digital information stored in the form of pixels. The image compression is the technique which can reduce size of the image without compromising quality of the image. The image compression techniques can classified into lossy and loss-less. In this research work, the technique is proposed which is SVD-WDR with PCA for lossy image compression. The PCA algorithm is applied which will select the extracted pixels from the image. The simulation of proposed technique is done in MATLAB and it has been analyzed that it performs well in terms of various parameters. The proposed and existing algorithms are implemented in MATLAB and it is been analyzed that proposed technique performs well in term of PSNR, MSE, SSIM and compression rate. In proposed technique the image is firstly compressed by WDR technique and then wavelet transform is applied on it. After extracting features with wavelet transform the patches are created and patches are sorted in order to perform compression by using decision tree. Decision tree sort the patches according to NRL order that means it define root node which maximum weight, left node which has less weight than root node and right node which has minimum weight. In this way the patches are sorted in descending order in terms of its weight (information). Now we can see the leaf nodes have the least amount of information (weight). In order to achieve compression of the image the leaf nodes which have least amount of information are discarded to reconstruct the image. Then inverse wavelet transform is applied to decompress the image. When the PCA technique is applied decision tree classifier the features which are not required are removed from the image in the efficient manner and increase compression ratio.
How to Cite
, K. A. A. B. D. M. R. “Image Compression Techniques Comparative Analysis Using SVD-WDR and SVD-WDR With Principal Component Analysis”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6, no. 2, Feb. 2018, pp. 111-6, doi:10.17762/ijritcc.v6i2.1432.