# How to Exploit the Transferability of Learned Image Compression to Conventional Codecs

arXiv: https://arxiv.org/abs/2012.01874 · HTML: https://mlsanity.com/paper/2012.01874/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

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

- [LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Network](https://mlsanity.com/paper/2112.06417/) (2021)
- [Deep Video Codec Control for Vision Models](https://mlsanity.com/paper/2308.16215/) (2023)
- [Standard compliant video coding using low complexity, switchable neural wrappers](https://mlsanity.com/paper/2407.07395/) (2024)
- [IQNet: Image Quality Assessment Guided Just Noticeable Difference Prefiltering For Versatile Video Coding](https://mlsanity.com/paper/2312.09799/) (2023)
- [Differentiable Proxy Learning for Adaptive Quantization Control in H.264 Video Coding](https://mlsanity.com/paper/2607.10478/) (2026)

## Abstract (arXiv)

Lossy image compression is often limited by the simplicity of the chosen loss measure. Recent research suggests that generative adversarial networks have the ability to overcome this limitation and serve as a multi-modal loss, especially for textures. Together with learned image compression, these two techniques can be used to great effect when relaxing the commonly employed tight measures of distortion. However, convolutional neural network based algorithms have a large computational footprint. Ideally, an existing conventional codec should stay in place, which would ensure faster adoption and adhering to a balanced computational envelope. As a possible avenue to this goal, in this work, we propose and investigate how learned image coding can be used as a surrogate to optimize an image for encoding. The image is altered by a learned filter to optimise for a different performance measure or a particular task. Extending this idea with a generative adversarial network, we show how entire textures are replaced by ones that are less costly to encode but preserve sense of detail. Our approach can remodel a conventional codec to adjust for the MS-SSIM distortion with over 20% rate improvement without any decoding overhead. On task-aware image compression, we perform favourably against a similar but codec-specific approach.