# Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization

arXiv: https://arxiv.org/abs/1904.03851 · HTML: https://mlsanity.com/paper/1904.03851/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning](https://mlsanity.com/paper/2112.08691/) (2021)
- [Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation](https://mlsanity.com/paper/2101.02121/) (2021)
- [RFD-ECNet: Extreme Underwater Image Compression with Reference to Feature Dictionar](https://mlsanity.com/paper/2308.08721/) (2023)

## Abstract (arXiv)

We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate. Our method leverages the "priors" at different resolution scale to improve the compression efficiency, and also employs the generative adversarial network(GAN) with multiscale discriminators to perform the end-to-end trainable rate-distortion optimization. We compare the perceptual quality of our reconstructions with traditional compression algorithms using High-Efficiency Video Coding(HEVC) based Intra Profile and JPEG2000 on the public Cityscapes and ADE20K datasets, demonstrating the significant subjective quality improvement.