The range of luminance levels in the natural world varies in the order of 108, significantly larger than the 8-bits employed by most digital imaging systems. To overcome their limited dynamic range traditional systems rely on the fact that the dynamic range of a scene is typically much lower, and by adjusting a global gain factor (shutter speed) it is possible to acquire usable images. However in many situations 8-bits of dynamic range is insufficient, meaning potentially useful information, lying outside of the dynamic range of the device, is lost. Traditional approaches to solving this have involved using nonlinear gamma tables to compress the range, hence reducing contrast in the digitized scene, or using 16-bit imaging devices, which use more bandwidth and are incompatible with most recording media and software post-processing techniques. This paper describes an algorithm, based on biological vision, which overcomes many of these problems. The algorithm reduces the redundancy of visual information and compresses the data observed in the real world into a significantly lower bandwidth signal, better suited for traditional 8-bit image processing and display. However, most importantly, no potentially useful information is lost and the contrast of the scene is enhanced in areas of high informational content (where there are changes) and reduced in areas containing low information content (where there are no changes). Thus making higher-order tasks, such as object identification and tracking, easier as redundant information has already been removed.