7 citations · 16 across the 7 of their papers we have counts for
7 papers
Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures
Jiaqi He, Zhihua Wang, Leon Wang +4
Contemporary color difference (CD) measures for photographic images typically operate by comparing co-located pixels, patches in a ``perceptually uniform'' color space, or features…
Learning a Deep Color Difference Metric for Photographic Images
Haoyu Chen, Zhihua Wang, Yang Yang +2
Most well-established and widely used color difference (CD) metrics are handcrafted and subject-calibrated against uniformly colored patches, which do not generalize well to photog…
Measuring Perceptual Color Differences of Smartphone Photographs
Zhihua Wang, Keshuo Xu, Yang Yang +5
Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophys…
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…