Image Compression Based on Data Folding and Principal Component Analysis

Abstract: Image compression assumes a fundamental part in image handling field particularly when we need to send the image through a system. While imaging methods produce restrictive measures of information and preparing expansive information is computationally costly, information compression is crucial instrument for capacity and correspondence purposes. Numerous present compression strategies give a high compression rates however with impressive loss of image quality. This paper displays a methodology for image compression in spatial space utilizing an idea of data folding. data folding procedure has been connected on shading images with various size. A rowfolding is connected on the grayimage grid took after by a column folding iteratively till the image size diminishes to predefined esteem as indicated by the levels of folding and unfolding iteration) reconstruction the original image). While Data unfolding process connected in adores mode. Then using principal component analysis as a statistical technique concerned with elucidating the covariance structure of a set of variables and uses orthogonal transformation to convert that set of observations of possibly correlated variables into a set of values of linearly uncorrelated and ordered variables called principal components. Method is tested on several standard test images and found that the quality of reconstructed image and compression ratio are ameliorated. The proposed Method is tried on a few standard test images and found that the nature of reproduced image and compression proportion are improved.