We have developed and tested two measures of visual clutter: the Feature Congestion measure, and the Subband Entropy measure.

Feature Congestion measure: This measure of visual clutter is based on the common experience of going to put a note on a colleague's desk. If the desk is uncluttered, it's easy to find a place to put the note where we are confident our colleague will notice it. However, if the desk is cluttered, we tend not to be confident they will notice the note, and perhaps will leave the note on a chair so they will spot it.

This suggests that clutter is related to the difficulty in adding an attention-grabbing item to a display. Visual search models typically attempt to predict the difficulty of searching for a particular target among particular distractors. However, our Statistical Saliency Model can easily make the dual prediction of how difficult it would be to add an attention-grabbing item to a display, and what features that item should have in order to draw attention. Our Feature Congestion measure of visual clutter is based upon this model of visual search.

Subband Entropy measure: This measure of visual clutter is based upon the intuition that a scene or display is less cluttered the more "organized" it is, i.e. the more items "group" together perceptually, whether through use of similar colors, or alignment, or other tricks. A related question to ask is to what extent each part of the display or scene is predictable from the rest of the scene? How redundant is the visual information in the scene?

 
mapping housing trends over time throughout the US