Ronnachai Jaroensri, Sylvain Paris, Aaron Hertzmann, V. Bychkovsky, F. Durand
{"title":"Predicting Range of Acceptable Photographic Tonal Adjustments","authors":"Ronnachai Jaroensri, Sylvain Paris, Aaron Hertzmann, V. Bychkovsky, F. Durand","doi":"10.1109/ICCPHOT.2015.7168372","DOIUrl":null,"url":null,"abstract":"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 overexposure, 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.","PeriodicalId":302766,"journal":{"name":"2015 IEEE International Conference on Computational Photography (ICCP)","volume":"1 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Computational Photography (ICCP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCPHOT.2015.7168372","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
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 overexposure, 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.