# Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization

Source: [Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization](<https://www.sota2.com/research/paper/68fa66e71e209131cea1309a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:01:21.007Z

Authors: Chao Huang, Haojie Liu, Tong Chen, Qiu Shen, Zhan Ma

Published: 2019-04-08T05:52:00.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1904.03851>)

[PDF](<https://arxiv.org/pdf/1904.03851.pdf>)