3 papers
cs.CV2026
IPAD-CLIP: Teaching CLIP to Detect Image Local Perceptual Artifacts
Juan Wang, Xinyu Sun, Ke Zhang +4
Current image quality assessment methods are heavily biased towards global distortions (e.g., noise, blur), neglecting local perceptual artifacts such as ghosting, lens flare, and…
cs.CV2024
SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning
Zewen Chen, Juan Wang, Wen Wang +8
Existing Image Quality Assessment (IQA) methods achieve remarkable success in analyzing quality for overall image, but few works explore quality analysis for Regions of Interest (R…
cs.CV2024
MobileIQA: Exploiting Mobile-level Diverse Opinion Network For No-Reference Image Quality Assessment Using Knowledge Distillation
Zewen Chen, Sunhan Xu, Yun Zeng +8
With the rising demand for high-resolution (HR) images, No-Reference Image Quality Assessment (NR-IQA) gains more attention, as it can ecaluate image quality in real-time on mobile…