7 citations · 9 across the 2 of their papers we have counts for
4 papers
Learning from Synthetic Data for Opinion-free Blind Image Quality Assessment in the Wild
Zhihua Wang, Zhi-Ri Tang, Jianguo Zhang +1
Nowadays, most existing blind image quality assessment (BIQA) models 1) are developed for synthetically-distorted images and often generalize poorly to authentic ones; 2) heavily r…
Semi-Supervised Deep Ensembles for Blind Image Quality Assessment
Zhihua Wang, Dingquan Li, Kede Ma
Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be "accurate" and "diverse." Here we investigate a semi-supervised ensem…
Troubleshooting Blind Image Quality Models in the Wild
Zhihua Wang, Haotao Wang, Tianlong Chen +2
Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When…
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment
Zhihua Wang, Kede Ma
The research in image quality assessment (IQA) has a long history, and significant progress has been made by leveraging recent advances in deep neural networks (DNNs). Despite high…