A quantitative image-level evaluation of multi-resolution 3D texture-based volume rendering

This research focuses on a quantitative evaluation of images produced by multi-resolution 3D texture-based volume rendering methods. Volume rendering techniques utilize nearly all the data in a volumetric data set to construct an image, so using coarser versions of the original data may negatively impact the display quality of the images produced. The trade-offs between a more efficient use of memory space needed to store a multi-resolution representation versus the potential sacrifice of image quality are characterized by visual inspection and by two image quality measurements: root mean square error (RMSE) and normalized mutual information (NMI). RMSE is a traditional image quality measurement and NMI is a recent technique used in image processing and human vision research that incorporates image entropies into a concise, intuitive information-based measurement to quantify information content. Using image entropy as a measure of information can help determine if there is some kind of structural artifact in the image, so it may compliment RMSE, which is often used to identify random error. The analysis of images produced from multi-resolution volume rendering experiments indicates that there is additional merit in looking at information-based measurements of image quality as well as using traditional measurements to identify and quantitatively evaluate regions of mismatch.