arXiv:2601. 21738v2 Announce Type: replace-cross Abstract: Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank-Order Correlation Coefficient (SRCC).
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.
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.
arXiv:2608.24881v1 Announce Type: cross Abstract: Generative models are commonly ranked by Fr\'echet Inception Distance (FID) and Kernel Inception Distance (KID), yet FID's first-two-moment summary c...
WebMRIQC is a browser-based, open‑source platform that wraps the MRIQC engine to provide automated MRI image quality assessment without local installation. It handles DICOM‑to‑BIDS conversion, runs the containerized MRIQC pipeline on a shared compute node, and presents results in an interactive dashboard that benchmarks each scan against normative data. Validation on BraTS‑Africa and BraTS 2021 datasets shows strong agreement across thirteen image‑quality metrics, indicating that the web implementation can match native MRIQC performance.
arXiv:2606. 16082v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have been increasingly adopted for Image Quality Assessment (IQA).
arXiv:2609.27959v1 Announce Type: cross Abstract: We investigate a local spectral-complexity representation for perceptual image quality assessment (IQA) based on Shannon entropy of singular values c...
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...
arXiv:2608. 02549v2 Announce Type: replace-cross Abstract: Efficient and perceptually meaningful quality assessment is a fundamental requirement for image and video processing, compression, and streaming systems.
Reliable quality control (QC) of magnetic resonance imaging (MRI) is essential for reliable diagnostic neuroimaging, yet standard manual assessment is subjective and time-consuming. MRIQC has establis...
arXiv:2609.25716v1 Announce Type: new Abstract: Reference-based image quality assessment (IQA) metrics aim to reflect how humans perceive the perceptual distance between a pair of images. To learn ho...
PreResQ‑R1 introduces a Preference‑Response Disentangled Reinforcement Learning framework for Visual Quality Assessment that jointly optimizes absolute score regression and relative ranking consistency. It employs a dual‑branch reward system—modeling intra‑sample response coherence and inter‑sample preference alignment—trained with Group Relative Policy Optimization. The method extends to video quality assessment via a global‑temporal and local‑spatial data flow strategy, achieving state‑of‑the‑art results on 10 IQA and 5 VQA benchmarks with only 6K images and 28K videos, and provides human‑aligned reasoning traces.
DANCo (Dimensionality from Angle and Norm Concentration) jointly calibrates nearest-neighbor distance and angular statistics and consistently reaches state-of-the-art accuracy on clean intrinsic-dimen...