In this work, we present an intuitive image-quality metric that is derived from the motivation of DVF visualization. It utilizes the features of the resulting image and effectively measures the similarity between the output of the visualization method and the input flow data. We use the angle between the gradient direction and the original vector field as a measure of such similarity and the gradient magnitude as an importance measure. Our metric enables the automatic evaluation of images for a given vector field and allows the comparison of different methods, parameters sets, and quality improvement strategies for a specific vector field. By integrating the metric into the image-computation process, our approach can be used to generate improved images by choosing the best parameter set. To verify the effectiveness of our method, we conducted an extensive user study that demonstrated the metric’s applicability to various situations. For instance, our approach elucidated the robustness of a DVF visualization in the presence of data-altering filters, such as resampling.

Dense flow visualization and streamlines are among the most popular methods in numerous applications of scientific visualization. Because very few works synthesize the benefits of these approaches into one framework, we propose a hybrid technique. In this work, we present a novel method of dense flow visualization that produces high contrast images (typical of streamline approaches), with manageable spatial frequency. We begin by introducing a notion of a streamline thickness function and thickness wave sources, and then compute the resulting image as a coherent wave interference pattern. We show that this problem is equivalent to finding the dominant eigenvector of a sparse matrix and employ the power iterations numerical scheme to demonstrate an efficient parallel implementation. We conclude by discussing possible quality improvement strategies and extensions of our method to other domains, including 3D flows.