Wavelets-Based Smoothness Metric for Volume Data

Abstract

In this paper we describe an objective smoothness assessment method for volume data. The metric can predict the extent of the difference in smoothness between a reference model, which may not be of perfect quality, and a distorted version. The proposed metric is based on the wavelet characterization of Besov function spaces. The comparison of Besov norm between two models can resolve the global and local differences in smoothness between them. Experimental results from volume datasets with smoothing and sharpening operations demonstrate its effectiveness. Also, the proposed smoothness index correlates well with human perceived vision when compared with direct volume rendered images.