# Generative Compression

arXiv: https://arxiv.org/abs/1703.01467 · HTML: https://mlsanity.com/paper/1703.01467/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2017.

## Built on by (most cited first, 30 of 40)

- [Enhanced Invertible Encoding for Learned Image Compression](https://mlsanity.com/paper/2108.03690/) (2021)
- [Multi-Realism Image Compression with a Conditional Generator](https://mlsanity.com/paper/2212.13824/) (2022)
- [An Introduction to Neural Data Compression](https://mlsanity.com/paper/2202.06533/) (2022)
- [High-Fidelity Image Compression with Score-based Generative Models](https://mlsanity.com/paper/2305.18231/) (2023)
- [Neural Video Compression using GANs for Detail Synthesis and Propagation](https://mlsanity.com/paper/2107.12038/) (2021)
- [Towards Semantic Communications: Deep Learning-Based Image Semantic Coding](https://mlsanity.com/paper/2208.04094/) (2022)
- [Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules](https://mlsanity.com/paper/2107.06463/) (2021)
- [Image Compression with Product Quantized Masked Image Modeling](https://mlsanity.com/paper/2212.07372/) (2022)
- [Extreme Generative Image Compression by Learning Text Embedding from Diffusion Models](https://mlsanity.com/paper/2211.07793/) (2022)
- [A coding theorem for the rate-distortion-perception function](https://mlsanity.com/paper/2104.13662/) (2021)
- [Extreme Image Compression using Fine-tuned VQGANs](https://mlsanity.com/paper/2307.08265/) (2023)
- [On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework](https://mlsanity.com/paper/2106.02782/) (2021)
- [SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Traffic](https://mlsanity.com/paper/2403.17933/) (2024)
- [Lossless data compression by large models](https://mlsanity.com/paper/2407.07723/) (2024)
- [Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaptation](https://mlsanity.com/paper/2406.00758/) (2024)
- [EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation](https://mlsanity.com/paper/2309.03244/) (2023)
- [Optimally Controllable Perceptual Lossy Compression](https://mlsanity.com/paper/2206.10082/) (2022)
- [Generative Visual Compression: A Review](https://mlsanity.com/paper/2402.02140/) (2024)
- [Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission](https://mlsanity.com/paper/2205.13120/) (2022)
- [Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility](https://mlsanity.com/paper/2207.04895/) (2022)
- [A Cross Channel Context Model for Latents in Deep Image Compression](https://mlsanity.com/paper/2103.02884/) (2021)
- [Learned Image Compression with Generalized Octave Convolution and Cross-Resolution Parameter Estimation](https://mlsanity.com/paper/2209.03353/) (2022)
- [Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models](https://mlsanity.com/paper/2111.03201/) (2021)
- [A New Image Codec Paradigm for Human and Machine Uses](https://mlsanity.com/paper/2112.10071/) (2021)
- [Video Coding Using Learned Latent GAN Compression](https://mlsanity.com/paper/2207.04324/) (2022)
- [Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization](https://mlsanity.com/paper/2403.13030/) (2024)
- [Synonymous Variational Inference for Perceptual Image Compression](https://mlsanity.com/paper/2505.22438/) (2025)
- [Substitutional Neural Image Compression](https://mlsanity.com/paper/2105.07512/) (2021)
- [You Can Mask More For Extremely Low-Bitrate Image Compression](https://mlsanity.com/paper/2306.15561/) (2023)
- [Learning Images Across Scales Using Adversarial Training](https://mlsanity.com/paper/2406.08924/) (2024)

## Abstract (arXiv)

Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. Here we describe the concept of generative compression, the compression of data using generative models, and suggest that it is a direction worth pursuing to produce more accurate and visually pleasing reconstructions at much deeper compression levels for both image and video data. We also demonstrate that generative compression is orders-of-magnitude more resilient to bit error rates (e.g. from noisy wireless channels) than traditional variable-length coding schemes.