- Feature Congestion metric: 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 metric: 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? With more organization, and thus more redundancy, the brain (or a computer) can represent an image with a more efficient encoding, which maintaining image quality. This suggests that the more cluttered an image, the more bits it should take to encode that image with something like JPEG2000. In fact, the Subband Entropy measure developed out of our observation that JPEG compressed file size was highly correlated with the Feature Congestion measure described above. The Subband Entropy measure of visual clutter is based upon these observations and intuitions. It decomposes an image into wavelet subbands, much like the decomposition early in the visual system. It then computes the entropy in each subband, and combines these to get total clutter for a given image or display.
The relevant links are: Press release, Matlab code and associated paper.