轻量级多指标无参考图像质量评估框架，适用于无人机图像快速筛选。
A Lightweight Multi-Metric No-Reference Image Quality Assessment Framework for UAV Imaging
- 融合模糊、边缘、噪声等七类可解释特征，生成0-100质量分
- 在5个数据集上皮尔逊相关系数达0.647至0.830，表现稳定
- 单图处理仅需1.97秒，内存占用随图像大小线性增长，适合部署
在大量图像自动采集的应用中，可靠的质量评估至关重要，但多数场景缺乏原始参考图像，因此无参考图像质量评估（NR-IQA）尤为关键。本文提出轻量级多指标图像质量评估（MM-IQA）框架，综合模糊、边缘结构、低分辨率伪影、曝光失衡、噪声、雾霾及频域内容等七类可解释特征，输出0-100范围内的单一质量分数。在五个基准数据集（KonIQ-10k、LIVE Challenge、KADID-10k、TID2013和BIQ2021）上测试，皮尔逊相关系数（SRCC）介于0.647至0.830之间。在合成农业数据集上的额外实验显示各评估线索行为一致。使用Python/OpenCV实现时，每幅图像处理耗时约1.97秒，且仅存储有限数量的灰度、滤波与频域中间表示，内存占用随图像尺寸线性增长。结果表明，MM-IQA可实现高效图像质量筛查，具备显式失真感知能力与较低计算开销。
原文摘要 · Abstract (English)
Reliable image quality assessment is essential in applications where large volumes of images are acquired automatically and must be filtered before further analysis. In many practical scenarios, a pristine reference image is unavailable, making no reference image quality assessment (NR-IQA) particularly important. This paper introduces Multi-Metric Image Quality Assessment (MM-IQA), a lightweight multi-metric framework for NR-IQA. It combines interpretable cues related to blur, edge structure, low resolution artifacts, exposure imbalance, noise, haze, and frequency content to produce a single quality score in the range [0,100].MM-IQA was evaluated on five benchmark datasets (KonIQ-10k, LIVE Challenge, KADID-10k, TID2013, and BIQ2021) and achieved SRCC values ranging from 0.647 to 0.830. Additional experiments on a synthetic agricultural dataset showed consistent behavior of the designed cues. The Python/OpenCV implementation required about 1.97 s per image. This method also has modest memory requirements because it stores only a limited number of intermediate grayscale, filtered, and frequency-domain representations, resulting in memory usage that scales linearly with image size. The results show that MM-IQA can be used for fast image quality screening with explicit distortion aware cues and modest computational cost.
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