  In this study, we address image retargeting, which is a task that adjusts
input images to arbitrary sizes. In one of the best-performing methods called
MULTIOP, multiple retargeting operators were combined and retargeted images at
each stage were generated to find the optimal sequence of operators that
minimized the distance between original and retargeted images. The limitation
of this method is in its tremendous processing time, which severely prohibits
its practical use. Therefore, the purpose of this study is to find the optimal
combination of operators within a reasonable processing time; we propose a
method of predicting the optimal operator for each step using a reinforcement
learning agent. The technical contributions of this study are as follows.
Firstly, we propose a reward based on self-play, which will be insensitive to
the large variance in the content-dependent distance measured in MULTIOP.
Secondly, we propose to dynamically change the loss weight for each action to
prevent the algorithm from falling into a local optimum and from choosing only
the most frequently used operator in its training. Our experiments showed that
we achieved multi-operator image retargeting with less processing time by three
orders of magnitude and the same quality as the original multi-operator-based
method, which was the best-performing algorithm in retargeting tasks.
