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.

图像编码是图像识别中的一种图像压缩技术，本文提出了一种名为 SA-ICM 的方法，其关注对象图像部分的边缘信息的编码和解码，该方法可用于各种图像识别模型，具有数据变化的鲁棒性，并从隐私角度提供保护。此外，SA-ICM 方法还可应用于训练用于视频压缩的 NeRV 模型，通过使用 Segment Anything 创建的边缘信息，可以创建适用于图像识别的 NeRV 模型（SA-NeRV）。实验结果证实 SA-ICM 在图像压缩方面的优势，并且 SA-NeRV 在机器的视频压缩中优于普通的 NeRV。