New vector morphological filtering operations are defined, and a set-theoretic analysis of these vector operations is presented. Abstract Histogram equalization and specification have been widely used to enhance information in a gray scale image, with histogram specification having the advantage of allowing the output histogram to be specified as compared to histogram equalization, which attempts to produce an output histogram that is uniform. Unfortunately, expanding histogram techniques to color images is not very straightforward. Since humans are sensitive to chromatic changes, care must be taken to ensure that incorrect colors are not produced. In this paper, we describe a method of extending gray level histogram specification to color images by performing histogram specification on the luminance, saturation, and hue components in the color difference C-Y color space.
Some key issues related to space-based compression design is discussed.
Various system considerations as well as potential compression options are also presented. A brief overview of a previously-reported robust lossy transform coding algorithm is given followed by the study of its performance sensitivities. In addition the impact of compression on the results of exploitation of environmental data including automated cloud study will be considered. It is shown that preprocessing to correct for geometric distortion noticeably improves the compression performance. Different grouping of bands also influences the performance.