Adjustment Space Exploration. There are many valid ways to adjust an image. Our two-dimensional adjustment space is shown on the left, with the origin (no adjustment) at the crosshairs. Four adjustments are shown in the space with Xs: two acceptable, two unacceptable. The magenta curve shows the boundary between acceptable and unacceptable adjustments, obtained by crowdsourcing: outside the boundary, the images no longer look acceptable. We present a machine learning algorithm to estimate this boundary for new images.

Abstract

There is often more than one way to select tonal adjust- ment for a photograph, and different individuals may prefer different adjustments. However, selecting good adjustments is challenging. This paper describes a method to predict whether a given tonal rendition is acceptable for a photograph, which we use to characterize its range of acceptable adjustments. We gathered a dataset of image "acceptability" over brightness and contrast adjustments. We find that unacceptable renditions can be explained in terms of over-exposure, under-exposure, and low contrast. Based on this observation, we propose a machine-learning algorithm to assess whether an adjusted photograph looks acceptable. We show that our algorithm can differentiate unsightly renditions from reasonable ones. Finally, we describe proof-of-concept applications that use our algorithm to guide the exploration of the possible tonal renditions of a photograph.

Publication

R. Jaroensri, S. Paris, A. Hertzmann, V. Bychkovsky, F. Durand "Predicting Range of Acceptable Photographic Tonal Adjustments" IEEE International Conference on Computational Photography (ICCP). April 2015, Houston, TX.

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Acknowledgement

The authors would like to thank Quanta Computer Inc. for their generous support of this project