# Learning Better Lossless Compression Using Lossy Compression

arXiv: https://arxiv.org/abs/2003.10184 · HTML: https://mlsanity.com/paper/2003.10184/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 30 of 33)

- [Language Modeling Is Compression](https://mlsanity.com/paper/2309.10668/) (2023)
- [GIVT: Generative Infinite-Vocabulary Transformers](https://mlsanity.com/paper/2312.02116/) (2023)
- [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)
- [Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression](https://mlsanity.com/paper/2209.04847/) (2022)
- [Learning Scalable ℓ_∞-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression](https://mlsanity.com/paper/2103.17015/) (2021)
- [Large Language Model for Lossless Image Compression with Visual Prompts](https://mlsanity.com/paper/2502.16163/) (2025)
- [Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data](https://mlsanity.com/paper/2410.05078/) (2024)
- [LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Network](https://mlsanity.com/paper/2112.06417/) (2021)
- [Partition and Code: learning how to compress graphs](https://mlsanity.com/paper/2107.01952/) (2021)
- [Large Language Models for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need](https://mlsanity.com/paper/2411.12448/) (2024)
- [Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/2405.20024/) (2024)
- [IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression](https://mlsanity.com/paper/2208.02519/) (2022)
- [CALLIC: Content Adaptive Learning for Lossless Image Compression](https://mlsanity.com/paper/2412.17464/) (2024)
- [Lossless Image Compression Using a Multi-Scale Progressive Statistical Model](https://mlsanity.com/paper/2108.10551/) (2021)
- [Reducing Redundancy in the Bottleneck Representation of the Autoencoders](https://mlsanity.com/paper/2202.04629/) (2022)
- [Split Hierarchical Variational Compression](https://mlsanity.com/paper/2204.02071/) (2022)
- [L3C-Stereo: Lossless Compression for Stereo Images](https://mlsanity.com/paper/2108.09422/) (2021)
- [DualComp: End-to-End Learning of a Unified Dual-Modality Lossless Compressor](https://mlsanity.com/paper/2505.16256/) (2025)
- [OmniZip: Learning a Unified and Lightweight Lossless Compressor for Multi-Modal Data](https://mlsanity.com/paper/2602.22286/) (2026)
- [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)
- [Learned Lossless Compression for JPEG via Frequency-Domain Prediction](https://mlsanity.com/paper/2303.02666/) (2023)
- [Learned Image Compression with Text Quality Enhancement](https://mlsanity.com/paper/2402.08643/) (2024)
- [SEEC: Segmentation-Assisted Multi-Entropy Models for Learned Lossless Image Compression](https://mlsanity.com/paper/2509.07704/) (2025)
- [OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression](https://mlsanity.com/paper/2111.01662/) (2021)
- [Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules](https://mlsanity.com/paper/2208.13974/) (2022)
- [GOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression](https://mlsanity.com/paper/2210.03301/) (2022)
- [Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block based on Early Stopping Discriminator](https://mlsanity.com/paper/2306.12757/) (2023)
- [FLLIC: Functionally Lossless Image Compression](https://mlsanity.com/paper/2401.13616/) (2024)

## Abstract (arXiv)

We leverage the powerful lossy image compression algorithm BPG to build a lossless image compression system. Specifically, the original image is first decomposed into the lossy reconstruction obtained after compressing it with BPG and the corresponding residual. We then model the distribution of the residual with a convolutional neural network-based probabilistic model that is conditioned on the BPG reconstruction, and combine it with entropy coding to losslessly encode the residual. Finally, the image is stored using the concatenation of the bitstreams produced by BPG and the learned residual coder. The resulting compression system achieves state-of-the-art performance in learned lossless full-resolution image compression, outperforming previous learned approaches as well as PNG, WebP, and JPEG2000.