Compression is based on the removal of redundancy inherent in most images. Most of the work in the area of image compression has been directed at removing the redundancy within the same image. However, if we examine similar images from MRI scans, or CT scans, or aerial images, from surveillance photos; there is often similarity between these images. Removing this inter-image redundancy is the focus of 'set compression' techniques. However, when similar images are registered their compression ratios are much higher, using set compression techniques. In order to register images we consider the Wavelet Modulus Maxima to determine the important control points which provide shift invariant structures. Using these structures and control points form different images in a similar set of images, we register the images using the homologous set of points. By suing the difference image we are able to exploit the inter-image redundancy. The difference image can be further compressed by any other available compression technique to further increase the compression ratio. Since we are dealing with sets of images, these applications are well suited for using parallel architectures. For example in similar sets of medical images, there is a great potential to exploit the inherent parallelism is the process, by working on several images simultaneously. We make use of several nodes on each image as well as process several images in parallel. This paper discusses the issues related to the parallel implementation of our image registration and compression algorithm. This paper also discussed the results and details the gains obtained in parallel implementation using several nodes.