Predicting Range of Acceptable Photographic Tonal Adjustments

Ronnachai Jaroensri, Sylvain Paris, Aaron Hertzmann, V. Bychkovsky, F. Durand
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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.
预测可接受的摄影色调调整范围
对于一张照片,通常有不止一种选择色调调整的方法,不同的人可能喜欢不同的调整。然而,选择好的调整是具有挑战性的。本文描述了一种方法来预测是否一个给定的色调再现是可接受的照片,我们用它来表征其可接受的调整范围。我们收集了一个关于亮度和对比度调整的图像“可接受性”的数据集。我们发现,不可接受的再现可以用曝光过度、曝光不足和低对比度来解释。基于这种观察,我们提出了一种机器学习算法来评估调整后的照片是否可以接受。我们证明了我们的算法可以区分不美观的场景和合理的场景。最后,我们描述了概念验证应用程序,这些应用程序使用我们的算法来指导对照片的可能色调再现的探索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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