# Learned Lossless Image Compression with a HyperPrior and Discretized Gaussian Mixture Likelihoods

arXiv: https://arxiv.org/abs/2002.01657 · HTML: https://mlsanity.com/paper/2002.01657/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression](https://mlsanity.com/paper/2404.13372/) (2024)
- [HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates](https://mlsanity.com/paper/2502.07160/) (2025)
- [Customizable ROI-Based Deep Image Compression](https://mlsanity.com/paper/2507.00373/) (2025)
- [Learned Lossless Compression for JPEG via Frequency-Domain Prediction](https://mlsanity.com/paper/2303.02666/) (2023)
- [Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules](https://mlsanity.com/paper/2208.13974/) (2022)

## Abstract (arXiv)

Lossless image compression is an important task in the field of multimedia communication. Traditional image codecs typically support lossless mode, such as WebP, JPEG2000, FLIF. Recently, deep learning based approaches have started to show the potential at this point. HyperPrior is an effective technique proposed for lossy image compression. This paper generalizes the hyperprior from lossy model to lossless compression, and proposes a L2-norm term into the loss function to speed up training procedure. Besides, this paper also investigated different parameterized models for latent codes, and propose to use Gaussian mixture likelihoods to achieve adaptive and flexible context models. Experimental results validate our method can outperform existing deep learning based lossless compression, and outperform the JPEG2000 and WebP for JPG images.