提出机器视觉质量评估新范式，打破人类偏好主导的局限
Image Quality Assessment: From Human to Machine Preference
- 定义机器视觉偏好：基于下游任务、模型和评测指标
- 构建225万标注的机器偏好数据库（MPD），含3万对参考/失真图像
- 揭示现有评分模型不适用于机器，适合计算机视觉研究者
基于人类主观偏好的图像质量评估（IQA）在过去几十年中得到广泛研究。然而，随着通信协议的发展，机器处理的视觉数据量已逐渐超过人类。对于机器而言，偏好取决于分割、检测等下游任务，而非视觉美感。鉴于人机视觉系统的巨大差异，本文首次提出机器视觉图像质量评估（Machine Vision IQA）这一课题。具体地，我们（1）定义了机器的主观偏好，包括下游任务、测试模型和评估指标；（2）建立了机器偏好数据库（MPD），包含225万细粒度标注和3万对参考/失真图像实例；（3）验证了主流IQA算法在MPD上的表现。实验表明，当前IQA度量标准以人类为中心，无法准确刻画机器偏好。我们诚挚希望MPD能推动IQA从人类偏好向机器偏好演进。项目页面见：https://github.com/lcysyzxdxc/MPD。
原文摘要 · Abstract (English)
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.
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