arXiv Computer Vision September 3, 2026 By Koffi Titus Sergio Aglin, Anthony K. Muchiri, Celestin Nkundineza

A Lightweight Multi-Metric No-Reference Image Quality Assessment Framework for UAV Imaging

Read the original on arXiv Computer Vision →

The paper presents MM‑IQA, a lightweight no‑reference image quality assessment framework designed for UAV imaging. It fuses interpretable metrics—blur, edge structure, low‑resolution artifacts, exposure imbalance, noise, haze, and frequency content—to output a single quality score between 0 and 100. Evaluated on five benchmark datasets, MM‑IQA achieved SRCC values from 0.647 to 0.830 and runs in about 1.97 s per image with modest memory usage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Hugging Face Trending Papers

Jun 29

LEIQ-Assessor: Multi-dimensional Quality Assessment of Low-light Enhanced Images via Multi-task Learning

Low-light image enhancement algorithms (LIEAs) aim to improve the visibility of images captured under poor illumination. However, the enhancement process often introduces artifacts such as noise amplification, color shift, structural damage, and over-exposure, which degrade the perceptual quality of the enhanced images.

llms computer-vision benchmarks

arXiv Machine Learning

Sep 10

AAMBERS-UAV: Acquisition-Aware Multimodal Backbone Evaluation and Ranking for UAV Weedy Rice Segmentation

arXiv:2609.05762v1 Announce Type: cross Abstract: UAV image collections contain spatially and temporally related frames, yet semantic-segmentation benchmarks commonly split them at image level. Such...

By Tarek Rahman, Nazim-E-Alam, Md Kishor Morol, Jannatun Noor

computer-vision multimodal benchmarks

arXiv Computer Vision

Aug 25

Bridge Damage Detection from Low-Light UAV Imagery via Degradation-Aware Mixture-of-Experts Enhancement

arXiv:2608.23136v1 Announce Type: new Abstract: Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspect...

By Hu Wang, Hongxu Pu, Zhiqi Hu, Fangzhou Lin, Wang Wang

robotics

arXiv Computer Vision

Aug 21

YolovN-CBi: A Lightweight and Efficient Architecture for Real-Time Detection of Small UAVs

arXiv:2512. 18046v2 Announce Type: replace Abstract: Unmanned Aerial Vehicles, commonly known as, drones pose increasing risks in civilian and defense settings, demanding accurate and real-time drone detection systems.

By Ami Pandat, Punna Rajasekhar, Gopika Vinod, Rohit Shukla

computer-vision robotics efficiency benchmarks

arXiv AI

Jun 16

Tool-IQA: Augmenting Image Quality Assessment with Simple Tools

arXiv:2606. 16082v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have been increasingly adopted for Image Quality Assessment (IQA).

By Guanyi Qin, Junjie Zhang, Chunming He, Yibing Fu, Jie Liang, Tianhe Wu, Lei Zhang

llms agents multimodal benchmarks

arXiv Computer Vision

Sep 15

MCIQA-2K: A Multi-Dimensional Dataset and No-Reference Quality Assessment Benchmark for Colorized Images

arXiv:2609.14495v1 Announce Type: new Abstract: Image colorization is an inherently ill-posed task, since a single grayscale image may correspond to multiple plausible colorized results. Consequently...

By Yunkai Zhuang, Qihang Yan, Zicheng Zhang, Guangtao Zhai

benchmarks

One email a morning, machine-written

Related stories

LEIQ-Assessor: Multi-dimensional Quality Assessment of Low-light Enhanced Images via Multi-task Learning

AAMBERS-UAV: Acquisition-Aware Multimodal Backbone Evaluation and Ranking for UAV Weedy Rice Segmentation

Bridge Damage Detection from Low-Light UAV Imagery via Degradation-Aware Mixture-of-Experts Enhancement

YolovN-CBi: A Lightweight and Efficient Architecture for Real-Time Detection of Small UAVs

Tool-IQA: Augmenting Image Quality Assessment with Simple Tools

MCIQA-2K: A Multi-Dimensional Dataset and No-Reference Quality Assessment Benchmark for Colorized Images