H
Hantao Liu
Researcher at Cardiff University
Publications - 106
Citations - 1781
Hantao Liu is an academic researcher from Cardiff University. The author has contributed to research in topics: Image quality & Computer science. The author has an hindex of 19, co-authored 85 publications receiving 1365 citations. Previous affiliations of Hantao Liu include Delft University of Technology & University of Hull.
Papers
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Journal ArticleDOI
Visual Attention in Objective Image Quality Assessment: Based on Eye-Tracking Data
Hantao Liu,Ingrid Heynderickx +1 more
TL;DR: Whether and to what extent the addition of NSS is beneficial to objective quality prediction in general terms is evaluated, and some practical issues in the design of an attention-based metric are addressed.
Journal ArticleDOI
A No-Reference Metric for Perceived Ringing Artifacts in Images
TL;DR: A novel no-reference metric that can automatically quantify ringing annoyance in compressed images is presented and shows to be highly consistent with subjective data.
Journal ArticleDOI
The Application of Visual Saliency Models in Objective Image Quality Assessment: A Statistical Evaluation
TL;DR: An exhaustive statistical evaluation is conducted to justify the added value of computational saliency in objective image quality assessment, using 20 state-of-the-art saliency models and 12 best-known IQMs, and provides useful guidance for applying saliency model dependence, IQM dependence, and image distortion dependence.
Proceedings ArticleDOI
Studying the added value of visual attention in objective image quality metrics based on eye movement data
Hantao Liu,Ingrid Heynderickx +1 more
TL;DR: Experimental results demonstrate that there is indeed a gain in performance including visual attention in objective metrics, and the amount of gain tends to depend on the type of objective metric and image distortion.
Journal ArticleDOI
A perceptually relevant no-reference blockiness metric based on local image characteristics
Hantao Liu,Ingrid Heynderickx +1 more
TL;DR: A novel no-reference blockiness metric that provides a quantitative measure of blocking annoyance in block-based DCT coding is presented and shows to be highly consistent with subjective data at a reduced computational load.