  When inspecting information visualizations under time critical settings, such
as emergency response or monitoring the heart rate in a surgery room, the user
only has a small amount of time to view the visualization "at a glance". In
these settings, it is important to provide a quantitative measure of the
visualization to understand whether or not the visualization is too "complex"
to accurately judge at a glance. This paper proposes Pixel Approximate Entropy
(PAE), which adapts the approximate entropy statistical measure commonly used
to quantify regularity and unpredictability in time-series data, as a measure
of visual complexity for line charts. We show that PAE is correlated with
user-perceived chart complexity, and that increased chart PAE correlates with
reduced judgement accuracy. We also find that the correlation between PAE
values and participants' judgment increases when the user has less time to
examine the line charts.
