A digital radiological image is made by a number of pixels, each of them characterized by a definite numerical value obtained by quantization which represents the luminance in that specific image unit. Spatial resolution and dynamic range are the main factors in determining the quality of a digital radiological image. The product of the two above factors defines the global dimensions of the image and is expressed in bits. In conventional radiographic images the global dimension of the image is expressed in MBytes, because of its high spatial and contrast resolution. To reduce the visualization and storage requirements as well as the transmission time of large image data sets, compression algorithms have been recently introduced. These algorithms are based on the fact that often in a digital image parts of the binary data are "redundant", that is they are not necessary for correct image representation. Therefore, compression methods are aimed at reducing both statistical and perceptive redundancy. Statistical redundancy is reduced by means of lossless coding which does not allow to compress images with a ratio higher than 4-5:1 and that--by definition--allows to recover the original image quality. On the other hand "lossy" compression algorithms, which eliminate the perceptive redundancy, are based on the reduction of spatial resolution and dynamic range and on transform-based methods. In particular, the latter have usually been more successful in terms of efficient compression, even when applied to conventional radiographic images. The basic transform procedure can be modified at various levels. JPEG is one of these methods, originally developed for photographic images, which can be usefully applied to radiological images as well. Lossy procedures allow to reach higher compression ratios than lossless methods, but the decrease in information content must be prevented from reducing diagnostic accuracy. In order to assess the diagnostic efficiency of the images compressed with lossy methods, semi-objective analyses are usually performed and ROC curves are produced and evaluated. A model ROC analysis is presented.
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