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We compare a variety of lossless image compression methods on a large sampleof astronomical images and show how the compression ratios and speeds of thealgorithms are affected by the amount of noise in the images. In the ideal casewhere the image pixel values have a random Gaussian distribution, theequivalent number of uncompressible noise bits per pixel is given by Nbits=log2(sigma * sqrt(12)) and the lossless compression ratio is given by R =BITPIX / Nbits + K where BITPIX is the bit length of the pixel values and K isa measure of the efficiency of the compression algorithm.  We perform image compression tests on a large sample of integer astronomicalCCD images using the GZIP compression program and using a newer FITStiled-image compression method that currently supports 4 compressionalgorithms: Rice, Hcompress, PLIO, and GZIP. Overall, the Rice compressionalgorithm strikes the best balance of compression and computational efficiency;it is 2--3 times faster and produces about 1.4 times greater compression thanGZIP. The Rice algorithm produces 75%--90% (depending on the amount of noise inthe image) as much compression as an ideal algorithm with K = 0.  The image compression and uncompression utility programs used in this study(called fpack and funpack) are publicly available from the HEASARC web site. Asimple command-line interface may be used to compress or uncompress any FITSimage file.

Lossless Astronomical Image Compression and the Effects of Noise