  Personalized and content-adaptive image enhancement can find many
applications in the age of social media and mobile computing. This paper
presents a relative-learning-based approach, which, unlike previous methods,
does not require matching original and enhanced images for training. This
allows the use of massive online photo collections to train a ranking model for
improved enhancement. We first propose a multi-level ranking model, which is
learned from only relatively-labeled inputs that are automatically crawled.
Then we design a novel parameter sampling scheme under this model to generate
the desired enhancement parameters for a new image. For evaluation, we first
verify the effectiveness and the generalization abilities of our approach,
using images that have been enhanced/labeled by experts. Then we carry out
subjective tests, which show that users prefer images enhanced by our approach
over other existing methods.
