Abstract: The aim of this study is to analyze and compare a set of wavelet families using for image compression. The study discusses important features of wavelet transform in compression of images. The wavelet transform is a fast developing tool for image compression and provides efficient compression performance. Especially for high compression ratios, wavelets perform much better than competing technologies both in terms of signal-to-noise ratio and visual quality. Wavelet transformation provides both spatial and frequency domain information of image. Wavelet Transform (WT) decomposes an image into wavelet function (wavelets) at different resolution levels. Therefore, the wavelet transform can be composed of function that satisfies requirements of multiresolution analysis. Depending on the application, different aspects of wavelets can be emphasized. The selection of wavelet for image compression depends on the image application and image contents. A review of the wavelet families using for image compression is given here. In this study we have analyzed various wavelets of different wavelet families (such as Biorthogonal, Daubechies, Reverse Biorthogonal, Symlets and Coiflets) performing image compression on variety of test images. The test images are of different frequency content, size and resolution. We have also analyzed effects of wavelet functions belonging to each of these wavelet families at a compression ratio of 100:1 at decomposition level 5 on the variety of test images. The results of compression performance for different wavelets of different wavelet family, image contents, compression ratios and resolutions are given. The image quality is measured, objectively peak signal-to-noise ratio and subjectively visual quality of image.