Perception-based Visual Quality Measures

In recent years diverse quality measures to support the exploration

of high-dimensional data sets have been proposed. Such measures

can be very useful to rank and select information-bearing projections

of very high dimensional data, when the visual exploration

of all possible projections becomes unfeasible. But even though a

ranking of the low dimensional projections may support the user in

the visual exploration task, different measures deliver different distances

between the views that do not necessarily match the expectations

of human perception. As an alternative solution, we propose

a perception-based approach that, similar to the existing measures,

can be used to select information bearing projections of the data.

Specifically, we construct a perceptual embedding for the different

projections based on the data from a psychophysics study and

multi-dimensional scaling. This embedding together with a ranking

function is then used to estimate the value of the projections for a

specific user task in a perceptual sense.