[paper] Perception and Painting: A Search for Effective, Engaging Visualizations

Christopher G. Healey and James T. Enns  [PDF]

Scientific visualization represents information as images that allow us to explore, discover, analyze, and validate large collections of data. Much of the research in this area is dedicated to the design of effective visualizations that support specific analysis needs. Recently, we have become interested in a new idea: Is a visualization beautiful? Can a visualization be considered a work of art?

One might expect answers to these questions to vary widely depending on the individual and their interpretation of what it means to be artistic. We believe that the issues of effectiveness and aesthetics may not be as independent as they might seem at first glance, however. Much can be learned from a study of two related disciplines: human psychophysics, and art theory and history. Perception teaches us how we “see” the world around us. Art history shows us how artistic masters captured our attention by designing works that evoke an emotional response. The common interest in visual attention provides an important bridge between these domains. We are using this bridge to produce visualizations that are both effective and engaging. This article describes our research, and discusses some of the lessons we have learned along the way.

Multidimensional Visualization

Work in our laboratory has studied various issues in scientific visualization for much of the last ten years. A large part of our effort has focused on multidimensional visualization, the need to visualize multiple layers of overlapping information simultaneously in a common display or image. We often divide this problem into two steps: (1) the design of a data-feature mapping M, a function that defines visual features (e.g., color, texture, or motion) to represent the data, and (2) an analysis of a viewer’s interpretation of the images M produces. An effective M generates visualizations that allow viewers to rapidly, accurate, and effortlessly explore their data.

One promising technique we have discovered is the use of results from human perception to predict the performance of a particular M. The low-level visual system identifies certain properties of what we see very quickly, often in only a few tenths of a second or less. Perhaps more importantly, this ability is display size insensitive; visual tasks are completed in a fixed length of time that is independent of the amount of information being displayed. Obviously, these findings are very attractive in a multidimensional visualization context. Different visual features can be combined to represent multiple data attributes. Large numbers of these “multidimensional data elements” can be packed into an image. Sequences of images are then rapidly analyzed by a viewer in a movie-like fashion.



Figure 1: Two examples of visualizing weather conditions: (a) traditional visualizations for each attribute composited into a single image; (b) simulated brush strokes that vary their color and texture to visualize the data

Fig. 1 shows two example visualizations of multidimensional weather data. The first image was constructed by taking traditional visualizations of each attribute, then compositing them together. Hue represents temperature (yellow for hot, green for cold), luminance represents pressure (bright for high, dark for low), directed contours represent wind direction, and Doppler radar traces represent precipitation. The second image was built using simulated brush strokes that vary their perceptual color and texture properties to visualize the data. Here, color represents temperature (bright pink for hot, dark green for cold), density represents pressure (denser for lower pressure), stroke orientation represents wind direction, and size represents precipitation (larger strokes for more rainfall). Although viewers often gravitate towards the first image due to its familiarity, any attempt to perform real analysis tasks leads to a rapid appreciation of the careful selection of colors and textures used in the second image. Experiments showed that viewers prefer the second image for the vast majority of the tasks we tested.

The use of perceptual guidelines can dramatically increase the amount of information we can visualize. We cannot take advantage of these strengths with an ad-hoc choice of M, however. Certain combinations of visual features actively mask information by interfering with our ability to see important properties of an image. A key goal, therefore, is to build guidelines on how to design effective visualizations, and to present these findings in a way that makes them accessible to other visualization researchers and practitioners.

An image that is seen as interesting or beautiful can encourage viewers to study it in detail.

Nonphotorealistic Visualization

explore in two directions:

  • nonphotorealistic rendering in computer graphics, and
  • art history and art theory discussions of known painterly styles.

We observed that many of the painterly styles we discovered seemed to have a close correspondence to visual features from our perceptual visualizations. For example, color and lighting in Impressionism have a direct relationship to the use of hue and luminance in visualization. Other styles like path, density, and length have partners like orientation, contrast, and size in perception. This suggested the following strategy to produce a visualization that is both effective and aesthetic:

  1. Produce a data-feature mapping M that uses the perceptual color and texture patterns that best represent a particular dataset and associated analysis tasks.
  2. Swap each visual feature in M with its corresponding painterly style.
  3. M now defines a mapping from data to painterly styles that control the visual appearance of computer-generated brush strokes; apply this mapping to produce a painted representation of the underlying dataset.


Although our initial experiments showed that our painterly visualizations are effective, we still had no evidence of their aesthetic merit. We ran a new set of experiments designed to investigate this property. These experiments studied three important questions:

  1. How artistic do viewers judge our painterly visualizations, relative to paintings by artistic masters?
  2. Can we identify any fundamental emotional factors that predict when viewers will perceive an image as artistic?
  3. Can we categorize individual viewers as preferring different types of art (e.g., realism or abstractionism), and how do these preferences impact the emotional responses that predict artistic rankings?



Figure 5. Example displays from the aesthetic judgement experiment: (a) a painterly visualization of weather conditions; (b) a nonphotorealistic rendering of a photograph of Lake Moraine in Banff, Canada

Our experiments asked viewers to order 28 images on a scale from 1 (lowest) to 7 (highest). We presented seven images from four different categories: master Impressionists (impressionism), master Abstractionists (abstractionism), nonphotorealistic renderings (nonphotorealism), and painterly visualizations (visualization).

An example of the painterly visualizations we tested is shown in Fig. 5a. Although real weather conditions are being visualized (temperature is represented by color, wind speed by coverage, pressure by size, and precipitation by orientation), no explanations were provided to our viewers about what was being depicted. We were careful to zoom in to a point where viewers would not interpret the image as part of a map. These images were classified as abstract in nature, since they had no obvious relationship to a real-world object or scene. They were paired against seven real paintings by master Abstractionists: one painting each by de Kooning, Johns, Malevich, Mondrain, and Pollock, and two by Kline.






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