# Adapting JPEG XS gains and priorities to tasks and contents

arXiv: https://arxiv.org/abs/2005.08768 · HTML: https://mlsanity.com/paper/2005.08768/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

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

- [Image coding for machines: an end-to-end learned approach](https://mlsanity.com/paper/2108.09993/) (2021)
- [Learned Image Coding for Machines: A Content-Adaptive Approach](https://mlsanity.com/paper/2108.09992/) (2021)
- [TACTIC: Joint Rate-Distortion-Accuracy Optimisation for Low Bitrate Compression](https://mlsanity.com/paper/2109.10658/) (2021)
- [Pruned Lightweight Encoders for Computer Vision](https://mlsanity.com/paper/2211.13137/) (2022)
- [Deep Selector-JPEG: Adaptive JPEG Image Compression for Computer Vision in Image classification with Human Vision Criteria](https://mlsanity.com/paper/2302.09560/) (2023)

## Abstract (arXiv)

Most current research in the domain of image compression focuses solely on achieving state of the art compression ratio, but that is not always usable in today's workflow due to the constraints on computing resources. Constant market requirements for a low-complexity image codec have led to the recent development and standardization of a lightweight image codec named JPEG XS. In this work we show that JPEG XS compression can be adapted to a specific given task and content, such as preserving visual quality on desktop content or maintaining high accuracy in neural network segmentation tasks, by optimizing its gain and priority parameters using the covariance matrix adaptation evolution strategy.