A Methodology for Comparing Direct Volume Rendering Algorithms Using a Projection-Based Data Level Approach

Abstract

Identifying and visualizing uncertainty together with the data is a well recognized problem. One of the culprits that introduce uncertainty in the visualization pipeline is the visualization algorithm itself. Uncertainties introduced in this way usually arise from approximations and manifest themselves as artifacts in the resulting images. In this paper, we focus on comparing different direct volume rendering (DVR) algorithms and their artifacts as a result of DVR algorithm selections and their associated parameter settings. We present a new data level comparison methodology that uses differences in intermediate rendering information. In particular, we extend the traditional image level comparison techniques to include data level comparison techniques. In image level comparisons, quantized pixel values are the starting point for comparison measurements. In contrast, data level comparison techniques have the advantage of accessing and evaluating the intermediate 3D information during the rendering process. Our data level approach overcomes limitations of image level approaches and provide capabilities to compare application dependent details as well as general rendering qualities. One of the key challenges with our data level comparison approach is finding a common base for comparing the rich variety of DVR algorithms. In this paper, we present how a projection algorithm can be used as a base for comparing other DVR algorithms. In addition, a set of projection-based metrics are derived to quantify the comparison measurements among DVR algorithms. The results presented in this paper complement our earlier findings where a ray-based approach was used as the base for comparing other DVR algorithms.