Performance analysis of image compression using wavelet thresholding

In this recent multimedia world, the significant challenges are the optimized use of storage space and Bandwidth. In order to reduce the storage space of images and transmission of information with limited Bandwidth availability, Image compression plays a imperative role by diminishing the size of image and to utilize the Bandwidth in economical and valuable manner without degrading the quality of image. In this paper we explore the performance analysis of different wavelet function for compression of images using wavelet thresholding. The wavelets used here are Biorthogonal 4.4, Haar, for decomposition of image. Hard and Soft thresholds are used for neglecting certain wavelet coefficients and the threshold methods used here are Global and Local thresholding. We analyzed the performance of this technique using metrics Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Compression Ratio (CR). The obtained results shows that local thresholding provides better reconstruction of image than Global thresholding.