Image Coding for Machines with Edge Information Learning Using Segment
  Anything
Takahiro Shindo Kein Yamada Taiju Watanabe Hiroshi Watanabe
Image Coding for Machines (ICM) is an image compression technique for image
recognition.
  This technique is essential due to the growing demand for image recognition
AI.
  In this paper, we propose a method for ICM that focuses on encoding and
decoding only the edge information of object parts in an image, which we call
SA-ICM.
  This is an Learned Image Compression (LIC) model trained using edge
information created by Segment Anything.
  Our method can be used for image recognition models with various tasks.
  SA-ICM is also robust to changes in input data, making it effective for a
variety of use cases.
  Additionally, our method provides benefits from a privacy point of view, as
it removes human facial information on the encoder's side, thus protecting
one's privacy.
  Furthermore, this LIC model training method can be used to train Neural
Representations for Videos (NeRV), which is a video compression model.
  By training NeRV using edge information created by Segment Anything, it is
possible to create a NeRV that is effective for image recognition (SA-NeRV).
  Experimental results confirm the advantages of SA-ICM, presenting the best
performance in image compression for image recognition.
  We also show that SA-NeRV is superior to ordinary NeRV in video compression
for machines.