A study of Performance Analysis of Image Compression Methods and Denoising Natural Image

Abstract

Image compression is in high demand as it reduces the computational time and consequently the cost in image storage and transmission. The basis for image compression is to remove redundant and unimportant data while to keep the compressed image quality in an acceptable range. Image compression is an application of data compression on digital images which is in highly demand as it reduces the computational time and consequently the cost in image storage and transmission. Wavelet Transform (WT) is used for compressing a natural image, De noise. Several wavelets (Haar, Daubechies, Coiflet, Symmlet and Dmey) are used for compressing and Denoising image. As a measure of performance Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR) are calculated between the original and compressed images. It is observed from our study that Haar performs better (lowest RMSE, highest PSNR and CR) than any other tested wavelets for natural Image. JPEG compression method performs 48.85% better than Haar wavelet.