# Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain

arXiv: https://arxiv.org/abs/2209.02514 · HTML: https://mlsanity.com/paper/2209.02514/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Conditional Probability Models for Deep Image Compression](https://mlsanity.com/paper/1801.04260/) (2018)
- [Enhanced Invertible Encoding for Learned Image Compression](https://mlsanity.com/paper/2108.03690/) (2021)
- [Joint Global and Local Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/2112.04487/) (2021)
- [Deep Image Compression using Decoder Side Information](https://mlsanity.com/paper/2001.04753/) (2020)
- [Neural Distributed Image Compression using Common Information](https://mlsanity.com/paper/2106.11723/) (2021)

## Built on by (most cited first, 5 of 5)

- [Perceptual Image Compression with Cooperative Cross-Modal Side Information](https://mlsanity.com/paper/2311.13847/) (2023)
- [V2X-DSC: Multi-Agent Collaborative Perception with Distributed Source Coding Guided Communication](https://mlsanity.com/paper/2602.00687/) (2026)
- [FFCA-Net: Stereo Image Compression via Fast Cascade Alignment of Side Information](https://mlsanity.com/paper/2312.16963/) (2023)
- [3D-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors](https://mlsanity.com/paper/2409.04013/) (2024)
- [Cross-Modal Attention Network with Dual Graph Learning in Multimodal Recommendation](https://mlsanity.com/paper/2601.11151/) (2026)

## Abstract (arXiv)

Beyond achieving higher compression efficiency over classical image compression codecs, deep image compression is expected to be improved with additional side information, e.g., another image from a different perspective of the same scene. To better utilize the side information under the distributed compression scenario, the existing method (Ayzik and Avidan 2020) only implements patch matching at the image domain to solve the parallax problem caused by the difference in viewing points. However, the patch matching at the image domain is not robust to the variance of scale, shape, and illumination caused by the different viewing angles, and can not make full use of the rich texture information of the side information image. To resolve this issue, we propose Multi-Scale Feature Domain Patch Matching (MSFDPM) to fully utilizes side information at the decoder of the distributed image compression model. Specifically, MSFDPM consists of a side information feature extractor, a multi-scale feature domain patch matching module, and a multi-scale feature fusion network. Furthermore, we reuse inter-patch correlation from the shallow layer to accelerate the patch matching of the deep layer. Finally, we nd that our patch matching in a multi-scale feature domain further improves compression rate by about 20% compared with the patch matching method at image domain (Ayzik and Avidan 2020).