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Due to the amount of medical image data being produced and transferredover networks, employing lossy compression has been accepted by worldwideregulatory bodies. As expected, increasing the degree of compression leadsto decreasing image fidelity. The extent of allowable irreversible compressionis dependent on the imaging modality and the nature of the image pathologyas well as anatomy. Interpolation, which often causes image distortion, hasbeen extensively used to rescale images during radiological diagnosis. Thiswork attempts to assess the quality of medical images after the applicationof lossy compression followed by rescaling. This research proposes a fullreference objective measure of quality for medical images that considerstheir deterministic and statistical properties. Statistical features are acquiredfrom the frequency domain of the signal and are combined with elements ofthe structural similarity index (SSIM). The aim is to construct a model that isspecialized for medical images and that could serve as a predictor of quality.

The fidelity of compressed and interpolated medical images