# End-to-end optimized image compression with competition of prior distributions

arXiv: https://arxiv.org/abs/2111.09172 · HTML: https://mlsanity.com/paper/2111.09172/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Channel-wise Autoregressive Entropy Models for Learned Image Compression](https://mlsanity.com/paper/2007.08739/) (2020)
- [Learning Independent Causal Mechanisms](https://mlsanity.com/paper/1712.00961/) (2017)
- [Practical Full Resolution Learned Lossless Image Compression](https://mlsanity.com/paper/1811.12817/) (2018)
- [A Unified End-to-End Framework for Efficient Deep Image Compression](https://mlsanity.com/paper/2002.03370/) (2020)
- [Computationally Efficient Neural Image Compression](https://mlsanity.com/paper/1912.08771/) (2019)
- [Image-Dependent Local Entropy Models for Learned Image Compression](https://mlsanity.com/paper/1805.12295/) (2018)
- [Fast and Flexible Image Blind Denoising via Competition of Experts](https://mlsanity.com/paper/1911.08724/) (2019)
- [Committee Draft of JPEG XL Image Coding System](https://mlsanity.com/paper/1908.03565/) (2019)

## Built on by (most cited first, 4 of 4)

- [LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Network](https://mlsanity.com/paper/2112.06417/) (2021)
- [On the Importance of Denoising when Learning to Compress Images](https://mlsanity.com/paper/2307.06233/) (2023)
- [Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset](https://mlsanity.com/paper/2501.08924/) (2025)
- [PULSE: Unlocking Practical Image Compression on Single-Thread CPU](https://mlsanity.com/paper/2609.18602/) (2026)

## Abstract (arXiv)

Convolutional autoencoders are now at the forefront of image compression research. To improve their entropy coding, encoder output is typically analyzed with a second autoencoder to generate per-variable parametrized prior probability distributions. We instead propose a compression scheme that uses a single convolutional autoencoder and multiple learned prior distributions working as a competition of experts. Trained prior distributions are stored in a static table of cumulative distribution functions. During inference, this table is used by an entropy coder as a look-up-table to determine the best prior for each spatial location. Our method offers rate-distortion performance comparable to that obtained with a predicted parametrized prior with only a fraction of its entropy coding and decoding complexity.