  Image cropping is essential in image editing for obtaining a compositionally
enhanced image. In display media, image cropping is a prospective technique for
automatically creating media content. However, image cropping for media
contents is often required to satisfy various constraints, such as an aspect
ratio and blank regions for placing texts or objects. We call this problem
image cropping under design constraints. To achieve image cropping under design
constraints, we propose a score function-based approach, which computes scores
for cropped results whether aesthetically plausible and satisfies design
constraints. We explore two derived approaches, a proposal-based approach, and
a heatmap-based approach, and we construct a dataset for evaluating the
performance of the proposed approaches on image cropping under design
constraints. In experiments, we demonstrate that the proposed approaches
outperform a baseline, and we observe that the proposal-based approach is
better than the heatmap-based approach under the same computation cost, but the
heatmap-based approach leads to better scores by increasing computation cost.
The experimental results indicate that balancing aesthetically plausible
regions and satisfying design constraints is not a trivial problem and requires
sensitive balance, and both proposed approaches are reasonable alternatives.
