Evaluation of a combined two- and three-dimensional compression method using human visual characteristics to yield high-quality 10:1 compression of cranial computed tomography scans

Rationale and Objectives. The compression of cranial computed tomography scans was improved by using independent intra- and interframe compression techniques. Methods. For intraframe compression, an image was decomposed into four subimages, one subimage was chosen as a reference subimage, and three of the subimages were predicted from the reference subimage. The prediction error was encoded with a classified vector quantizer (CVQ) based on human visual perception characteristics. Interframe redundancy is exploited by a displacement estimated interslice (DEI) algorithm that encodes the differences between reference subimages from adjacent slices. This combined DEI/CVQ method was subjectively evaluated by 13 radiologists under a blinded protocol, and was compared to the CVQ method alone, the DEI method alone, the original images, and to a standard intraframc discrete cosine transform (DCT) compression method. Results. Only the combined DEI/CVQ method at 10:1 compression was not scored significantly different from the original images. At 15:1 compression, the DEI/CVQ method was scored significantly better than the 10:1 DCT and any other 15:1 compression methods. Conclusions. Compressed image quality is enhanced by exploiting inter- and intraframc redundancy, and by modeling.