  In recent years, learning-based color and tone enhancement methods for photos
have become increasingly popular. However, most learning-based image
enhancement methods just learn a mapping from one distribution to another based
on one dataset, lacking the ability to adjust images continuously and
controllably. It is important to enable the learning-based enhancement models
to adjust an image continuously, since in many cases we may want to get a
slighter or stronger enhancement effect rather than one fixed adjusted result.
In this paper, we propose a quality-guided image enhancement paradigm that
enables image enhancement models to learn the distribution of images with
various quality ratings. By learning this distribution, image enhancement
models can associate image features with their corresponding perceptual
qualities, which can be used to adjust images continuously according to
different quality scores. To validate the effectiveness of our proposed method,
a subjective quality assessment experiment is first conducted, focusing on skin
tone adjustment in portrait photography. Guided by the subjective quality
ratings obtained from this experiment, our method can adjust the skin tone
corresponding to different quality requirements. Furthermore, an experiment
conducted on 10 natural raw images corroborates the effectiveness of our model
in situations with fewer subjects and fewer shots, and also demonstrates its
general applicability to natural images. Our project page is
https://github.com/IntMeGroup/quality-guided-enhancement .
