# Lossless Image Compression through Super-Resolution

arXiv: https://arxiv.org/abs/2004.02872 · HTML: https://mlsanity.com/paper/2004.02872/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 25 of 25)

- [Zero-Shot Detection of AI-Generated Images](https://mlsanity.com/paper/2409.15875/) (2024)
- [MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model](https://mlsanity.com/paper/2402.16749/) (2024)
- [iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression](https://mlsanity.com/paper/2103.16211/) (2021)
- [iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder](https://mlsanity.com/paper/2111.00965/) (2021)
- [Efficient Visual Computing with Camera RAW Snapshots](https://mlsanity.com/paper/2212.07778/) (2022)
- [LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Network](https://mlsanity.com/paper/2112.06417/) (2021)
- [Methods and Trends in Detecting AI-Generated Images: A Comprehensive Review](https://mlsanity.com/paper/2502.15176/) (2025)
- [Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion](https://mlsanity.com/paper/2311.16168/) (2023)
- [Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection](https://mlsanity.com/paper/2502.07778/) (2025)
- [Training-free Detection of Generated Videos via Spatial-Temporal Likelihoods](https://mlsanity.com/paper/2603.15026/) (2026)
- [Lossless Image Compression Using a Multi-Scale Progressive Statistical Model](https://mlsanity.com/paper/2108.10551/) (2021)
- [Split Hierarchical Variational Compression](https://mlsanity.com/paper/2204.02071/) (2022)
- [Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain](https://mlsanity.com/paper/2203.16357/) (2022)
- [Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation](https://mlsanity.com/paper/2107.07975/) (2021)
- [L3C-Stereo: Lossless Compression for Stereo Images](https://mlsanity.com/paper/2108.09422/) (2021)
- [PixelPyramids: Exact Inference Models from Lossless Image Pyramids](https://mlsanity.com/paper/2110.08787/) (2021)
- [End-to-end lossless compression of high precision depth maps guided by pseudo-residual](https://mlsanity.com/paper/2201.03195/) (2022)
- [Leveraging progressive model and overfitting for efficient learned image compression](https://mlsanity.com/paper/2210.04112/) (2022)
- [FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution](https://mlsanity.com/paper/2211.16678/) (2022)
- [Learned Lossless Compression for JPEG via Frequency-Domain Prediction](https://mlsanity.com/paper/2303.02666/) (2023)
- [SEEC: Segmentation-Assisted Multi-Entropy Models for Learned Lossless Image Compression](https://mlsanity.com/paper/2509.07704/) (2025)
- [GOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression](https://mlsanity.com/paper/2210.03301/) (2022)
- [Challenges and Solutions in Selecting Optimal Lossless Data Compression Algorithms](https://mlsanity.com/paper/2509.25219/) (2025)
- [GIC-DLC: Differentiable Logic Circuits for Hardware-Friendly Grayscale Image Compression](https://mlsanity.com/paper/2601.14130/) (2026)
- [RAWIC: Bit-Depth Adaptive Lossless Raw Image Compression](https://mlsanity.com/paper/2603.28105/) (2026)

## Abstract (arXiv)

We introduce a simple and efficient lossless image compression algorithm. We store a low resolution version of an image as raw pixels, followed by several iterations of lossless super-resolution. For lossless super-resolution, we predict the probability of a high-resolution image, conditioned on the low-resolution input, and use entropy coding to compress this super-resolution operator. Super-Resolution based Compression (SReC) is able to achieve state-of-the-art compression rates with practical runtimes on large datasets. Code is available online at https://github.com/caoscott/SReC.