33 citations · 52 across the 24 of their papers we have counts for
7 papers · 1 filter
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results
Ivan Molodetskikh, Artem Borisov, Dmitriy Vatolin +24
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 E…
Assessing UHD Image Quality from Aesthetics, Distortions, and Saliency
Wei Sun, Weixia Zhang, Yuqin Cao +6
UHD images, typically with resolutions equal to or higher than 4K, pose a significant challenge for efficient image quality assessment (IQA) algorithms, as adopting full-resolution…
Dual-Branch Network for Portrait Image Quality Assessment
Wei Sun, Weixia Zhang, Yanwei Jiang +8
Portrait images typically consist of a salient person against diverse backgrounds. With the development of mobile devices and image processing techniques, users can conveniently ca…
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
Marcos V. Conde, Saman Zadtootaghaj, Nabajeet Barman +33
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based method…
AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment
Chunyi Li, Tengchuan Kou, Yixuan Gao +10
With the rapid advancements in AI-Generated Content (AIGC), AI-Generated Images (AIGIs) have been widely applied in entertainment, education, and social media. However, due to the…
A Comparative Study of Perceptual Quality Metrics for Audio-driven Talking Head Videos
Weixia Zhang, Chengguang Zhu, Jingnan Gao +3
The rapid advancement of Artificial Intelligence Generated Content (AIGC) technology has propelled audio-driven talking head generation, gaining considerable research attention for…
When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents
Weixia Zhang, Dingquan Li, Guangtao Zhai +2
Contemporary no-reference image quality assessment (NR-IQA) models can effectively quantify perceived image quality, often achieving strong correlations with human perceptual score…