Comparative color space analysis of difference images from adjacent visible human slices for lossless compression

This paper reports the compression ratio performance of the RGB, YIQ, and HSV color plane models for the lossless coding of the National Library of Medicine's Visible Human (VH) color data set. In a previous study the correlation between adjacent VH slices was exploited using the RGB color plane model. The results of that study suggested an investigation into possible improvements using the other two color planes, and alternative differencing methods. YIQ and HSV, also know a HSI, both represent the image by separating the intensity from the color information, and we anticipated higher correlation between the intensity components of adjacent VH slices. However the compression ratio did not improve by the transformation from RGB into the other color plane models, since in order to maintain lossless performance, YIQ and HSV both require more bits to store each pixel. This increase in file size is not offset by the increase in compression due to the higher correlation of the intensity value, the best performance being achieved with the RGB color plane model. This study also explored three methods of differencing: average reference image, alternating reference image, and cascaded difference from single reference. The best method proved to be the first iteration of the cascaded difference from single reference. In this method, a single reference image is chosen, and the difference between it and its neighbor is calculated. Then the difference between the neighbor and its next neighbor is calculated. This method requires that all preceding images up to the reference image be reconstructed before the target image is available. The compression ratios obtained from this method are significantly better than the competing methods.