Effects of preprocessing applied to the compression of ultraspectral images

Abstract

One common approach to the compression of ultraspectral data cubes is by means of schemes where linear prediction plays an important role in facilitating the removal of redundant information. In general, compression algorithms can be seen as a sequence of stages where the output of one stage is the input of the following one. A stage that implements linear prediction relies heavily on a preprocessing stage that acts as a reversible procedure that rearranges the data cube and maximizes its spectral band correlation. In this paper we focus on AIRS (Atmospheric Infrared Sounder) images, a type of ultraspectral data cube, that involve more than two thousand bands and are excellent candidates to compression. Specifically we take into consideration several elements that are part of the preprocessing stage of an ultraspectral image. First, we explore the effect of SFCs (Space Filling Curves) as a way to provide a method to map an m-dimensional space into a highly correlated unidimensional space. In order to improve the overall mapping performance we propose a new scanning procedure that provides a more efficient alternative to the use of traditional state of the art curves. Second, we analyze, compare and introduce modifications to different band ordering and correlation estimation methods presented in the context of ultraspectral image preprocessing. Finally, we apply the techniques presented in this paper to a real AIRS compression architecture to obtain rate-distortion curves as a function of preprocessing parameters and determine the best scenario for a given linear prediction stage.