mlsanityData to 14 Oct 2026

Image Quality Assessment: From Human to Machine Preference

arXiv 2503.10078 ↗First posted 13 Mar 2025cs.CV cs.MM eess.IVcited by 12

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Abstract (arXiv)

Image Quality Assessment (IQA) based on human subjective preferences has undergone extensive research in the past decades. However, with the development of communication protocols, the visual data consumption volume of machines has gradually surpassed that of humans. For machines, the preference depends on downstream tasks such as segmentation and detection, rather than visual appeal. Considering the huge gap between human and machine visual systems, this paper proposes the topic: Image Quality Assessment for Machine Vision for the first time. Specifically, we (1) defined the subjective preferences of machines, including downstream tasks, test models, and evaluation metrics; (2) established the Machine Preference Database (MPD), which contains 2.25M fine-grained annotations and 30k reference/distorted image pair instances; (3) verified the performance of mainstream IQA algorithms on MPD. Experiments show that current IQA metrics are human-centric and cannot accurately characterize machine preferences. We sincerely hope that MPD can promote the evolution of IQA from human to machine preferences. Project page is on: https://github.com/lcysyzxdxc/MPD.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 67 of its 92 references that are in the map. First posted on arXiv 13 Mar 2025.

Builds on

67 of 92 references.

References · 67

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 8.

12 papers.

  1. 2025RGC-VQA: An Exploration Database for Robotic-Generated Video Quality Assessmentcited by 4
  2. 2025Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identificationcited by 2
  3. 2025ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizercited by 1
  4. 2026rPPG-VQA: A Video Quality Assessment Framework for Unsupervised rPPG Trainingcited by 1
  5. 2026MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Qualitycited by 1
  6. 2025Image Quality Assessment for Machines: Paradigm, Large-scale Database, and Modelscited by 0
  7. 2026GT-PCQA: Geometry-Texture Decoupled Point Cloud Quality Assessment with MLLMcited by 0
  8. 2026Beyond Fidelity: Semantic Similarity Assessment in Low-Level Image Processingcited by 0
  9. 2026ML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Qualitycited by 0
  10. 2026FDIR: Harmonizing Fidelity and Human-Machine Preference in Lossy Compression Image Restorationcited by 0
  11. 2026CAT-Flow: Curvature-Adaptive sTeps for Flow Matchingcited by 0
  12. 2026TaskGuard: Task-Conditioned Restoration Utility for Risk-Aware Object Detectioncited by 0