A Metric for the Evaluation of Dense Vector Field Visualizations

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