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In this study, we address image retargeting, which is a task that adjustsinput images to arbitrary sizes. In one of the best-performing methods calledMULTIOP, multiple retargeting operators were combined and retargeted images ateach stage were generated to find the optimal sequence of operators thatminimized the distance between original and retargeted images. The limitationof this method is in its tremendous processing time, which severely prohibitsits practical use. Therefore, the purpose of this study is to find the optimalcombination of operators within a reasonable processing time; we propose amethod of predicting the optimal operator for each step using a reinforcementlearning agent. The technical contributions of this study are as follows.Firstly, we propose a reward based on self-play, which will be insensitive tothe large variance in the content-dependent distance measured in MULTIOP.Secondly, we propose to dynamically change the loss weight for each action toprevent the algorithm from falling into a local optimum and from choosing onlythe most frequently used operator in its training. Our experiments showed thatwe achieved multi-operator image retargeting with less processing time by threeorders of magnitude and the same quality as the original multi-operator-basedmethod, which was the best-performing algorithm in retargeting tasks.

Self-Play Reinforcement Learning for Fast Image Retargeting