Performance Evaluation of Lossless Compression Techniques: An Application of Satellite Images

Satellite images are larger in size and it needs high amount of storage space and transmission time. There is a greater challenge to store or transmit the satellite images from the satellite to earth station. Image compression techniques have evolved to effectively process the images with tolerable or no loss in quality. The satellite images can be compressed to manage the storage space and communication bandwidth. Though several researches have been done on compression of natural images, only few have concentrated on satellite images. The nature of satellite images poses a greater challenge to compress satellite images. To carry out this work, we have used a satellite image dataset which consists of 2800 images of ships in satellite imagery with ship or no-ship classification. The existing image compression techniques such as Lempel Ziv Markov chain Algorithm (LZMA), Burrows Wheeler Transform (BWT), Lempel Ziv Welch (LZW) coding, Deflate and LZ77 are compared to one another. The comparison results imply that LZMA achieve better compression than other methods with the compression ratio, compression factor and compression time of 0.5666, 1.765 and 53 seconds respectively.