This paper introduces new terminology to describe the perceptual qualities of the non-photorealistic animation sequences created using an analysis/synthesis approach to rendering. Specifically, we propose the use of different matching optimization criteria as part of the creative control for generating animated sequences, or stylized video, and we explore the perceptual differences that are obtained when different optimization criteria are used. Additionally, metrics are introduced that describe the strengths and weakness of each of these matching strategies. Moreover, we show that these metrics may be useful for future evaluations of stylized video. We examine a series of sequences generated using different matching algorithms based on these metrics, and a user evaluation of 30 participants demonstrates that our objective metrics are perceptually relevant.
The density of points within multidimensional clusters can impact the effective representation of distances and groups when
projecting data from higher dimensions onto a lower dimensional space. This paper examines the use of motion to retain
an accurate representation of the point density of clusters that might otherwise be lost when a multidimensional dataset is
projected into a 2D space. We investigate how users interpret motion in 2D scatterplots and whether or not they are able to
effectively interpret the point density of the clusters through motion. Specifically, we consider different types of <i>density-based
motion</i>, where the magnitude of the motion is directly related to the density of the clusters. We conducted a series
of user studies with synthetic datasets to explore how motion can help users in various multidimensional data analyses,
including cluster identification, similarity seeking, and cluster ranking tasks. In a first user study, we evaluated the motions
in terms of task success, task completion times, and subject confidence. Our findings indicate that, for some tasks, motion
outperforms the static scatterplots; circular path motions in particularly give significantly better results compared to the
other motions. In a second user study, we found that users were easily able to distinguish clusters with different densities
as long as the magnitudes of motion were above a particular threshold. Our results indicate that it may be effective to
incorporate motion into visualization systems that enable the exploration and analysis of multidimensional data.