Predicting Range of Acceptable Photographic Tonal Adjustments
| Autor: | Ronnachai Jaroensri, Sylvain Paris, Frédo Durand, Vladimir Bychkovsky, Aaron Hertzmann |
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| Přispěvatelé: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | MIT web domain ICCP |
| Popis: | © 2015 IEEE. There is often more than one way to select tonal adjustment 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. |
| Databáze: | OpenAIRE |
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