You’ve Seen Enough: Quality-Constrained Image Coding For Machines

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, 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 penalty function that guides the quality toward the target: 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 BD-rates of −22.82% and −29.81% relative to an unconstrained joint rate–distortion–task optimization and a simple rate–distortion baseline, respectively, 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.

BibTeX

@misc{phamdinh2026quality,
  author        = {Pham-Dinh, Khoa and Nami, Sanaz and
                   Rezazadegan Tavakoli, Hamed and Gabbouj, Moncef and
                   Pakdaman, Farhad},
  title         = {You've Seen Enough: Quality-Constrained Image Coding For Machines},
  year          = {2026},
  eprint        = {2609.25108},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2609.25108}
}