Abstract: The objective of this study is to evaluate a set of wavelets for image compression. Image compression using wavelet transforms results in an improved compression ratio. Wavelet transformation is the technique that provides both spatial and frequency domain information. These properties of wavelet transform greatly help in identification and selection of significant and non-significant coefficients amongst the wavelet coefficients. DWT (Discrete Wavelet Transform) represents image as a sum of wavelet function (wavelets) on different resolution levels. So, the basis of wavelet transform can be composed of function that satisfies requirements of multiresolution analysis. Depending on the application, different aspects of wavelets can be emphasized. There exists a large selection of wavelet families, depending on the choice of wavelet function. The choice of wavelet function for image compression depends on the image application and the content of image. A review of the fundamentals of image compression based on wavelet is given here. This study also discussed important features of wavelet transform in compression of images. In this study we have evaluate and compare seven different wavelet families i.e., Haar, Daubechies, Symlets, Coiflets, Biorthogonal, Reverse Biorthogonal and discrete approximation of Meyer on variety of test images set. We have also analyzed effects of wavelet functions belonging to each of these wavelet families on image quality at a compression ratio of 10:1 and 100:1 on the variety of test images set at decomposition level 5. Image quality is measured, objectively using peak signal-to-noise ratio and subjectively using visual image quality. Our results provide a reference for application developers to choose an application based wavelet for image compression for their applications.