3 March 2008 Global nonlinear compression of natural luminances in painted art
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Over the past quarter century, measures of statistical regularities of natural scenes have emerged as important tools in explaining the coding properties of the mammalian visual system. Such measures have recently been extended to the study of art. Our own work has shown that a log nonlinearity is a reasonable first approximation of the type of luminance compression that artists perform when they create images. But how does this nonlinearity compare to those that artists actually use? In this paper, we propose a model of the global luminance compression strategy used by one artist. We also compare the curves required to transform natural scenes so that the scene luminance histograms matched the histograms of a number of collections of art, and we tested the response of observers to those scenes. The collections included a group of Hudson River School paintings; a group of works deemed to be "abstract" works in a forced-choice paradigm; collections of paintings from the Eastern and Western hemispheres; and other classes. If a single transform were sufficient to compress images in the way artists do, we would expect these transforms all to be log-like and on average, there should be little or no difference in observer preference for the collection of natural scenes when they are compressed according to these transforms. We find instead that these groupings of art have distinct transforms and that Western observers prefer many of these transforms over a log transform. Together these findings offer evidence that a painter's global luminance compression strategy-or "artist's look-up table"-may be a fundamental property of a given painter or grouping of paintings, though further study is needed to establish what factors determine the shape of this transform. We discuss a number of possible factors.
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Daniel J. Graham, Daniel J. Graham, David J. Field, David J. Field, } "Global nonlinear compression of natural luminances in painted art", Proc. SPIE 6810, Computer Image Analysis in the Study of Art, 68100K (3 March 2008); doi: 10.1117/12.766636; https://doi.org/10.1117/12.766636


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