CT images represent a unique challenge for medical image compression; they have many pixels with very high and very low intensity values, often with sharp edges between the two, and the intensity values have quantitative significance, representing the attenuation coefficient in Hounsfield units (HU). Thus, the intensity ranges which represent bone or various soft tissues are essentially known in advance. When viewing a CT image, different window and level settings for mapping the 12-bit intensity values to an 8-bit display are used, depending on the objects of interest. When viewing objects with very high or low values, large window values are used, so that differences in intensity values on the order of 10 or 20 HU are not significant and are scarcely noticed in practice. Conversely, when viewing soft tissues, small windows are used to capture subtle but important distinction, and an intensity difference of 10-20 HU can be highly significant. CT compression schemes, therefore, should have a mechanism to increase the representation accuracy of intensity values corresponding to soft tissue relative to those corresponding to bone and air. We describe a simple technique to force compression algorithms to assign more importance to specific intensity ranges by transforming the histogram of the image prior to compression, and show sample results. The technique significantly increases the ratio by which the images can be compressed while retaining acceptable image quality at both large and small window settings in common clinical use.