AN ADAPTIVE TECHNIQUE TO MAXIMIZE LOSSLESS IMAGE DATA-COMPRESSION OF SATELLITE IMAGES

Data compression will play an increasingly important role in the stora ge and transmission of image data within the NASA science programs as the Earth Observing System comes into operation. It is important that the science data be preserved at the fidelity the instrument and satel lite communication systems were designed to produce. Lossless compress ion must therefore be applied, at least to archive the processed instr ument data. In this paper, we present an analysis of the performance o f lossless compression techniques and develop an adaptive approach tha t applies image remapping, feature-based image segmentation to determi ne regions of similar entropy, as well as high-order arithmetic coding , to obtain significant improvements over the use of conventional comp ression techniques alone. Image remapping is used to transform the ori ginal image into a lower entropy state. Several techniques were tested on satellite images including differential pulse code modulation, bi- linear interpolation, and block-based linear predictive coding. The re sults of these experiments are discussed, and trade-offs between compu tation requirements and entropy reductions are used to identify the op timum approach for a variety of satellite images. Further entropy redu ction can be achieved by segmenting the image based on local entropy p roperties and then applying a coding technique that maximizes compress ion for the region. Experimental results are presented showing the eff ect of different coding techniques for regions of different entropy. A rule-base is developed through which the technique giving the best co mpression is selected. The paper concludes that maximum compression ca n be achieved cost effectively and at acceptable performance rates wit h a combination of techniques that are selected based on image context ual information.