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SelPh: Progressive Learning and Support of Manual Photo Color Enhancement

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Published on May 31, 2016

SelPh: Progressive Learning and Support of Manual Photo Color Enhancement
Yuki Koyama, Daisuke Sakamoto, Takeo Igarashi

CHI '16: ACM Conference on Human Factors in Computing Systems
Session: Enabling End-Users and Designers

Abstract
Color enhancement is a very important aspect of photo editing. Even when photographers have tens of or hundreds of photographs, they must enhance each photo one by one by manually tweaking sliders in software such as brightness and contrast, because automatic color enhancement is not always satisfactory for them. To support this repetitive manual task, we present self-reinforcing color enhancement, where the system implicitly and progressively learns the user's preferences by training on their photo editing history. The more photos the user enhances, the more effectively the system supports the user. We present a working prototype system called SelPh, and then describe the algorithms used to perform the self-reinforcement. We conduct a user study to investigate how photographers would use a self-reinforcing system to enhance a collection of photos. The results indicate that the participants were satisfied with the proposed system and strongly agreed that the self-reinforcing approach is preferable to the traditional workflow.

DOI:: http://dx.doi.org/10.1145/2858036.285...
WEB:: https://chi2016.acm.org/

Recorded at the 2016 CHI Conference on Human Factors in Computing Systems in San Jose, CA, United States, May 7-12, 2016

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