5 papers
Q-CLIP: Unleashing the Power of Vision-Language Models for Video Quality Assessment through Unified Cross-Modal Adaptation
Yachun Mi, Yu Li, Yanting Li +6
Accurate and efficient Video Quality Assessment (VQA) has long been a key research challenge. Current mainstream VQA methods typically improve performance by pretraining on large-s…
Segmenting and Understanding: Region-aware Semantic Attention for Fine-grained Image Quality Assessment with Large Language Models
Chenyue Song, Chen Hui, Haiqi Zhu +4
No-reference image quality assessment (NR-IQA) aims to simulate the process of perceiving image quality aligned with subjective human perception. However, existing NR-IQA methods e…
MS-IQA: A Multi-Scale Feature Fusion Network for PET/CT Image Quality Assessment
Siqiao Li, Chen Hui, Wei Zhang +7
Positron Emission Tomography / Computed Tomography (PET/CT) plays a critical role in medical imaging, combining functional and anatomical information to aid in accurate diagnosis.…
LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images
Chenyue Song, Chen Hui, Qing Lin +8
Autoregressive Initial Bits is a framework that integrates sub-image autoregression and latent variable modeling, demonstrating its advantages in lossless medical image compression…
BPCLIP: A Bottom-up Image Quality Assessment from Distortion to Semantics Based on CLIP
Chenyue Song, Chen Hui, Wei Zhang +4
Image Quality Assessment (IQA) aims to evaluate the perceptual quality of images based on human subjective perception. Existing methods generally combine multiscale features to ach…