An adaptive technique to maximize lossless image data compression of satellite images

Abstract

Data compression will play an increasingly important role in the storage 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 satellite communication systems were designed to produce. Lossless compression must therefore be applied, at least to archive the processed instrument data. In this paper, we present an analysis of the performance of lossless compression techniques and develop an adaptive approach that applies image remapping, feature-based image segmentation to determine regions of similar entropy, as well as high-order arithmetic coding, to obtain significant improvements over the use of conventional compression techniques alone. Image remapping is used to transform the original 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 results of these experiments are discussed, and trade-offs between computation requirements and entropy reductions are used to identify the optimum approach for a variety of satellite images. Further entropy reduction can be achieved by segmenting the image based on local entropy properties and then applying a coding technique that maximizes compression for the region. Experimental results are presented showing the effect of different coding techniques for regions of different entropy. A rule-base is developed through which the technique giving the best compression is selected. The paper concludes that maximum compression can be achieved cost effectively and at acceptable performance rates with a combination of techniques that are selected based on image contextual information.