Adapting JPEG XS gains and priorities to tasks and contents
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 May 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1.
5 papers.
- 2021Image coding for machines: an end-to-end learned approachcited by 47
- 2021Learned Image Coding for Machines: A Content-Adaptive Approachcited by 21
- 2021TACTIC: Joint Rate-Distortion-Accuracy Optimisation for Low Bitrate Compressioncited by 0
- 2022Pruned Lightweight Encoders for Computer Visioncited by 0
- 2023Deep Selector-JPEG: Adaptive JPEG Image Compression for Computer Vision in Image classification with Human Vision Criteriacited by 0
- 2023Deep Selector-JPEG: Adaptive JPEG Image Compression for Computer Vision in Image classification with Human Vision Criteria19 Feb 2023
- 2022Pruned Lightweight Encoders for Computer Vision23 Nov 2022
- 2021TACTIC: Joint Rate-Distortion-Accuracy Optimisation for Low Bitrate Compression22 Sep 2021
- 2021Image coding for machines: an end-to-end learned approach23 Aug 2021
- 2021Learned Image Coding for Machines: A Content-Adaptive Approach23 Aug 2021