5 papers · 1 filter
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…
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…
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…
PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts
Zewen Chen, Haina Qin, Juan Wang +4
Due to the diversity of assessment requirements in various application scenarios for the IQA task, existing IQA methods struggle to directly adapt to these varied requirements afte…
GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for No-reference Image Quality Assessment
Zewen Chen, Juan Wang, Bing Li +7
Due to the subjective nature of image quality assessment (IQA), assessing which image has better quality among a sequence of images is more reliable than assigning an absolute mean…