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Algorithms for compression of hyperspectral data are commonly evaluated on a readily available collection of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images. These images are the end product of processing raw data from the instrument, and their sample value distributions contain artificial regularities that are introduced by the conversion of raw data values to radiance units. It is shown that some of the best reported lossless compression results for these images are achieved by algorithms that significantly exploit these artifacts. This fact has not been widely reported and may not be widely recognized. Compression performance comparisons involving such algorithms and these standard AVIRIS images can be misleading if they are extrapolated to images that lack such artifacts, such as unprocessed hyperspectral images. In fact, two of these algorithms are shown to achieve rather unremarkable compression performance on a set of more recent AVIRIS images that do not contain appreciable calibration-induced artifacts. This newer set of AVIRIS images, which contains both calibrated and raw images, is made available for compression experiments. To underscore the potential impact of exploiting calibration-induced artifacts in the standard AVIRIS data sets, a compression algorithm is presented that achieves noticeably smaller compressed sizes for these data sets than is reported for any other algorithm.