# Learning to Improve Image Compression without Changing the Standard Decoder

arXiv: https://arxiv.org/abs/2009.12927 · HTML: https://mlsanity.com/paper/2009.12927/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Differentiable JPEG: The Devil is in the Details](https://mlsanity.com/paper/2309.06978/) (2023)
- [LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Network](https://mlsanity.com/paper/2112.06417/) (2021)
- [An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems](https://mlsanity.com/paper/2201.11782/) (2022)
- [Extreme Compression of Adaptive Neural Images](https://mlsanity.com/paper/2405.16807/) (2024)
- [Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder](https://mlsanity.com/paper/2201.11795/) (2022)
- [JPEG Processing Neural Operator for Backward-Compatible Coding](https://mlsanity.com/paper/2507.23521/) (2025)
- [JPEG-Inspired Cloud-Edge Holography](https://mlsanity.com/paper/2512.12367/) (2025)

## Abstract (arXiv)

In recent years we have witnessed an increasing interest in applying Deep Neural Networks (DNNs) to improve the rate-distortion performance in image compression. However, the existing approaches either train a post-processing DNN on the decoder side, or propose learning for image compression in an end-to-end manner. This way, the trained DNNs are required in the decoder, leading to the incompatibility to the standard image decoders (e.g., JPEG) in personal computers and mobiles. Therefore, we propose learning to improve the encoding performance with the standard decoder. In this paper, We work on JPEG as an example. Specifically, a frequency-domain pre-editing method is proposed to optimize the distribution of DCT coefficients, aiming at facilitating the JPEG compression. Moreover, we propose learning the JPEG quantization table jointly with the pre-editing network. Most importantly, we do not modify the JPEG decoder and therefore our approach is applicable when viewing images with the widely used standard JPEG decoder. The experiments validate that our approach successfully improves the rate-distortion performance of JPEG in terms of various quality metrics, such as PSNR, MS-SSIM and LPIPS. Visually, this translates to better overall color retention especially when strong compression is applied. The codes are available at https://github.com/YannickStruempler/LearnedJPEG.