A PREDICTIVE CLASSIFIED VECTOR QUANTIZER AND ITS SUBJECTIVE QUALITY EVALUATION FOR X-RAY CT IMAGES

We have developed a new classified vector quantizer (CVQ) using decomp osition and prediction which does not need to store or transmit any si de information, To obtain better quality in the compressed images, hum an visual perception characteristics are applied to the classification and bit allocation, This CVQ has been subjectively evaluated for a se quence 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 evaluati on data are analyzed statistically with analysis of variance and Tukey 's multiple comparison, Even though there are large variations in judg ing image quality among readers, the proposed algorithm has shown sign ificantly better quality than the DCT at a statistical significance le vel of 0.05, Only an interframe CVQ can reproduce the quality of the o riginals at 10:1 compression at the same significance level, While the CVQ can reproduce compressed images that are not statistically differ ent from the originals in quality, the effect on diagnostic accuracy r emains to be investigated.