A predictive classified vector quantizer and its subjective quality evaluation for X-ray CT images

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Abstract
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.

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