# Perceptually Optimizing Deep Image Compression

arXiv: https://arxiv.org/abs/2007.02711 · HTML: https://mlsanity.com/paper/2007.02711/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

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

- [Lossy Compression for Lossless Prediction](https://mlsanity.com/paper/2106.10800/) (2021)
- [Deep Perceptual Image Quality Assessment for Compression](https://mlsanity.com/paper/2103.01114/) (2021)
- [Video Coding Using Learned Latent GAN Compression](https://mlsanity.com/paper/2207.04324/) (2022)
- [Learnt Deep Hyperparameter selection in Adversarial Training for compressed video enhancement with perceptual critic](https://mlsanity.com/paper/2302.14516/) (2023)

## Abstract (arXiv)

Mean squared error (MSE) and ℓp norms have largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used to assess visual information loss, these simple norms are not highly consistent with human perception. Here, we propose a different proxy approach to optimize image analysis networks against quantitative perceptual models. Specifically, we construct a proxy network, which mimics the perceptual model while serving as a loss layer of the network.We experimentally demonstrate how this optimization framework can be applied to train an end-to-end optimized image compression network. By building on top of a modern deep image compression models, we are able to demonstrate an averaged bitrate reduction of 28.7% over MSE optimization, given a specified perceptual quality (VMAF) level.