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