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The authors have developed a new classified vector quantizer (CVQ) using decomposition and prediction which does not need to store or transmit any side information. To obtain better quality in the compressed images, human visual perception characteristics are applied to the classification and bit allocation. This CVQ has been subjectively evaluated for a sequence of X-ray CT images and compared to a DCT coding method. Nine X-ray CT head images from three patients are compressed at 10:1 and 15:1 compression ratios and are evaluated by 13 radiologists. The evaluation data are analyzed statistically with analysis of variance and Tukey's multiple comparison. Even though there are large variations in judging image quality among readers, the proposed algorithm has shown significantly better quality than the DCT at a statistical, significance level of 0.05. Only an interframe CVQ can reproduce the quality of the originals at 10:1 compression at the same significance level. While the CVQ can reproduce compressed images that are not statistically different from the originals in quality, the effect on diagnostic accuracy remains to be investigated.

Displacement estimated interframe (DEI) coding, a coding scheme for 3-D medical image data sets such as X-ray computed tomography (CT) or magnetic resonance (MR) images, is presented. To take advantage of the correlation between contiguous slices, a displacement-compensated difference image based on the previous image is encoded. The best fitting distribution functions for the discrete cosine transform (DCT) coefficients obtained from displacement compensated difference images are determined and used in allocating bits and optimizing quantizers for the coefficients. The DEI scheme is compared with 2-D block discrete cosine transform (DCT) as well as a full-frame DCT using the bit allocation technique of S. Lo and H.K. Huang (1985). For X-ray CT head images, the present bit allocation and quantizer design, using an appropriate distribution model, resulted in a 13-dB improvement in the SNR compared to the full-frame DCT using the bit allocation technique. For an image set with 5-mm slice thickness, the DEI method gave about 5% improvement in the compression ratio on average and less blockiness at the same distortion. The performance gain increases to about 10% when the slice thickness decreases to 3 mm.