Search Results for author:
Found 7 papers, 1 papers with code
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results
This paper presents the Video Super-Resolution (SR) Quality Assessment (QA) Challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment
Specifically, we find a more compact and reliable space i. e., feature style space for perception-oriented UDA based on an interesting/amazing observation, that the feature style (i. e., the mean and variance) of the deep layer in DNNs is exactly associated with the quality score in NR-IQA.
Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment
Thanks to the development of unsupervised domain adaptation (UDA), some works attempt to transfer the knowledge from a label-sufficient source domain to a label-free target domain under domain shift with UDA.
Blind Image Quality Assessment
Unsupervised Domain Adaptation
SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment
In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space.
Compressed Image Quality Assessment
Full-Reference Image Quality Assessment
+1
FAN: Frequency Aggregation Network for Real Image Super-resolution
Specifically, we extract different frequencies of the LR image and pass them to a channel attention-grouped residual dense network (CA-GRDB) individually to output corresponding feature maps.
LIRA: Lifelong Image Restoration from Unknown Blended Distortions
Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task.
