Predicting Range of Acceptable Photographic Tonal Adjustments

Ronnachai Jaroensri
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.


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.


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.


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