# Deep Image Compression using Decoder Side Information

arXiv: https://arxiv.org/abs/2001.04753 · HTML: https://mlsanity.com/paper/2001.04753/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform](https://mlsanity.com/paper/2108.09551/) (2021)
- [Neural Distributed Image Compression with Cross-Attention Feature Alignment](https://mlsanity.com/paper/2207.08489/) (2022)
- [Neural Distributed Source Coding](https://mlsanity.com/paper/2106.02797/) (2021)
- [Learning-Driven Lossy Image Compression; A Comprehensive Survey](https://mlsanity.com/paper/2201.09240/) (2022)
- [LDMIC: Learning-based Distributed Multi-view Image Coding](https://mlsanity.com/paper/2301.09799/) (2023)
- [Neural Distributed Image Compression using Common Information](https://mlsanity.com/paper/2106.11723/) (2021)
- [Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain](https://mlsanity.com/paper/2209.02514/) (2022)
- [Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information](https://mlsanity.com/paper/2310.04311/) (2023)
- [Distributed Image Transmission using Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2201.10340/) (2022)
- [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)
- [Distributed Image Compression with Multimodal Side Information at Extremely Low Bitrates](https://mlsanity.com/paper/2605.22061/) (2026)

## Abstract (arXiv)

We present a Deep Image Compression neural network that relies on side information, which is only available to the decoder. We base our algorithm on the assumption that the image available to the encoder and the image available to the decoder are correlated, and we let the network learn these correlations in the training phase. Then, at run time, the encoder side encodes the input image without knowing anything about the decoder side image and sends it to the decoder. The decoder then uses the encoded input image and the side information image to reconstruct the original image. This problem is known as Distributed Source Coding in Information Theory, and we discuss several use cases for this technology. We compare our algorithm to several image compression algorithms and show that adding decoder-only side information does indeed improve results. Our code is publicly available at https://github.com/ayziksha/DSIN.