Image Coding for Machines with Object Region Learning
Takahiro Shindo Taiju Watanabe Kein Yamada Hiroshi Watanabe
Compression technology is essential for efficient image transmission and
storage. With the rapid advances in deep learning, images are beginning to be
used for image recognition as well as for human vision. For this reason,
research has been conducted on image coding for image recognition, and this
field is called Image Coding for Machines (ICM). There are two main approaches
in ICM: the ROI-based approach and the task-loss-based approach. The former
approach has the problem of requiring an ROI-map as input in addition to the
input image. The latter approach has the problems of difficulty in learning the
task-loss, and lack of robustness because the specific image recognition model
is used to compute the loss function. To solve these problems, we propose an
image compression model that learns object regions. Our model does not require
additional information as input, such as an ROI-map, and does not use
task-loss. Therefore, it is possible to compress images for various image
recognition models. In the experiments, we demonstrate the versatility of the
proposed method by using three different image recognition models and three
different datasets. In addition, we verify the effectiveness of our model by
comparing it with previous methods.