Image Compression addresses the problem of reducing the amount of data required to represent the digital image. Image compression and decompression are very popular processes in image processing. Image compression is a way in which the data to be transmitted are compressed into a smaller version and then transmitted. Compression is achieved by the removal of one or more of three basic data redundancies: (1) Coding redundancy, which is present when less than optimal (i.e. the smallest length) code words are used; (2) Interpixel redundancy, which results from correlations between the pixels of an image & (3) psycho visual redundancy which is due to data that is ignored by the human visual system. In order to be useful, a compression algorithm has a corresponding decompression algorithm that reproduces the original file once the compressed file is given. Image decompression is the reconstruction of the compressed data into its original form. As the image compression may suffer loss, the decompression also needs to be taken cared so that even if loss occurs, the reconstruction of the compressed image to its original form is possible. In this paper we present two algorithms which can be applied for compressed image reconstruction.