The present investigation reports the results of entropy coding for lossless compression of the visible human (VH) color data set for archival purposes where any loss of information is not desirable. One of the main objectives of this investigation was to determine the role of a unique feature i.e. high correlation between adjacent VH slices in designing a lossless compression algorithm. This study demonstrates that lossless JPEG provides better compression of individual slices than the difference images despite low entropy content of the difference images. This may be attributed to abrupt variation in gray levels in difference images rather than smoothly varying individual images which are more suitable for lossless JPEG format. Huffman coding of the difference image frames provides a general idea of the compressibility of 3D predictive coding. However, a combination of binary arithmetic and predictive coding that takes advantage of the similarity between adjacent frames may yield the most efficient, lossless compression scheme for the VH slices.