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The demand for handling images in digital form has increased dramatically in recent years. Thanks to performance improvements and significant reductions in the cost of image scanners, photographs, printed text, and other media can now be easily converted into digital form (image digitization). Direct acquisition of digital images (scene digitization) is also becoming more common as sensors and associated electronics improve; the use of satellite imaging, e.g., LANDSAT, in remote sensing of the earth and the advent of electronic still-cameras in the consumer market are good examples. In addition, many different imaging modalities in medicine, such as computed tomography (CT) or magnetic resonance imaging (MRI), generate images directly in digital form. Computer-generated images (synthetic images) are also becoming a major source of digital data. The use of computer graphics in advertising and entertainment is widespread, and its use in scientific visualization and engineering applications is growing at a rapid pace. The reason for this interest in digital images is clear: representing images in digital form allows visual information to be easily manipulated in useful and novel ways. This fact, combined with the exponential growth in computing power over the past decade, has resulted in the use of digital imaging systems in such diverse fields as astronomy, remote sensing, medicine, photojournalism, graphic arts, law enforcement, advertisement, and manufacturing.
Despite the advantages, there is one potential problem with digital images, namely, the large number of bits required to represent them. Fortunately, digital images, in their canonical representation, generally contain a significant amount of redundancy. Image compression, which is the art/science of efficient coding of picture data, aims at taking advantage of this redundancy to reduce the number of bits required to represent an image.
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