2026-09-19T05:49:49Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/998622022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Frédo Durand.
Jaroensri, Ronnachai
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2015-11-09T19:53:44Z
2015-11-09T19:53:44Z
2015
2015
http://hdl.handle.net/1721.1/99862
927759866
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 57-58).
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 thesis 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.
by Ronnachai Jaroensri.
S.M.
58 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Predicting range of acceptable photographic tonal adjustments
Thesis
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<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 thesis 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.</Abstract>
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