  Encoding the Region Of Interest (ROI) with better quality than the background
has many applications including video conferencing systems, video surveillance
and object-oriented vision tasks. In this paper, we propose a ROI-based image
compression framework with Swin transformers as main building blocks for the
autoencoder network. The binary ROI mask is integrated into different layers of
the network to provide spatial information guidance. Based on the ROI mask, we
can control the relative importance of the ROI and non-ROI by modifying the
corresponding Lagrange multiplier $ \lambda $ for different regions.
Experimental results show our model achieves higher ROI PSNR than other methods
and modest average PSNR for human evaluation. When tested on models pre-trained
with original images, it has superior object detection and instance
segmentation performance on the COCO validation dataset.
