You've Seen Enough: Quality-Constrained Image Coding for Machines
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, we cap human-observed quality at a desired level and devote the remaining bits to machine performance. Specifically, joint compression-segmentation training is recast 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. This paper proposes two variants of a pena...
| Authors | Khoa Pham-Dinh, Sanaz Nami, Hamed Rezazadegan Tavakoli et al. |
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| Primary category | cs.CV |
| Published | 20 September 2026 |
| Last updated at source | 24 September 2026 |
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