[Submitted on 20 Sep 2026]
Title:You've Seen Enough: Quality-Constrained Image Coding for Machines
View a PDF of the paper titled You've Seen Enough: Quality-Constrained Image Coding for Machines, by Khoa Pham-Dinh and 4 other authors
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Abstract:Visual data is increasingly consumed by machine-vision systems rather than by human observers. Image Coding for Machines (ICM) compresses images assuming the main observer is a computer vision application and that the human observer needs to inspect or validate the decisions. Inspired by just-noticeable distortion, which sets the quality to the just-acceptable level for human observers, we aim to cap the human-observed quality at a desired level, with the goal of using the remaining coding capacity to improve the machine performance. We recast joint compression-segmentation training as a constrained optimization problem in which the codec must meet a predefined acceptable target visual quality while a task term consumes the remaining coding capacity. We solve this by designing a penalty function to guide the quality to the desired target. We propose two penalty functions, an absolute function and a bilinear function, the latter applying a steeper slope once the target visual quality is exceeded. Experimental results show that, under the quality constraint, the proposed method achieves a BD-rate of $-22.82\%$ over an unconstrained joint rate--distortion--task optimization and $-29.81\%$ over a simple rate--distortion baseline, showcasing bitrate reduction with the same task performance. This is achieved while the codec also meets the target visual quality with a reasonable error and without adding any complexity overhead.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.25108 [cs.CV]
(or arXiv:2609.25108v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.25108
arXiv-issued DOI via DataCite
Submission history
From: Khoa Pham-Dinh [view email] [v1] Sun, 20 Sep 2026 04:02:36 UTC (7,810 KB)
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