# ROI-based Deep Image Compression with Swin Transformers

arXiv: https://arxiv.org/abs/2305.07783 · HTML: https://mlsanity.com/paper/2305.07783/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 12 of 12)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [High-Fidelity Generative Image Compression](https://mlsanity.com/paper/2006.09965/) (2020)
- [CompressAI: a PyTorch library and evaluation platform for end-to-end compression research](https://mlsanity.com/paper/2011.03029/) (2020)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [Transformer-based Image Compression](https://mlsanity.com/paper/2111.06707/) (2021)
- [Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform](https://mlsanity.com/paper/2108.09551/) (2021)
- [Object-Based Image Coding: A Learning-Driven Revisit](https://mlsanity.com/paper/2003.08033/) (2020)

## Built on by (most cited first, 10 of 10)

- [Image Coding for Machines with Edge Information Learning Using Segment Anything](https://mlsanity.com/paper/2403.04173/) (2024)
- [Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression](https://mlsanity.com/paper/2511.06424/) (2025)
- [Image Coding for Machines with Object Region Learning](https://mlsanity.com/paper/2308.13984/) (2023)
- [Delta-ICM: Entropy Modeling with Delta Function for Learned Image Compression](https://mlsanity.com/paper/2410.07669/) (2024)
- [Customizable ROI-Based Deep Image Compression](https://mlsanity.com/paper/2507.00373/) (2025)
- [SLIM: Semantic-based Low-bitrate Image compression for Machines by leveraging diffusion](https://mlsanity.com/paper/2512.18200/) (2025)
- [Guided Diffusion for the Extension of Machine Vision to Human Visual Perception](https://mlsanity.com/paper/2503.17907/) (2025)
- [Progressive Learned Image Compression for Machine Perception](https://mlsanity.com/paper/2512.20070/) (2025)
- [ROIX-Comp: Optimizing X-ray Computed Tomography Imaging Strategy for Data Reduction and Reconstruction](https://mlsanity.com/paper/2602.15917/) (2026)
- [End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines](https://mlsanity.com/paper/2603.29927/) (2026)

## Abstract (arXiv)

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  λ  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.