J
Jiheng Wang
Researcher at University of Waterloo
Publications - 30
Citations - 533
Jiheng Wang is an academic researcher from University of Waterloo. The author has contributed to research in topics: Image quality & Stereoscopy. The author has an hindex of 11, co-authored 30 publications receiving 446 citations. Previous affiliations of Jiheng Wang include Food and Drug Administration.
Papers
More filters
Journal ArticleDOI
SSIM-Based Coarse-Grain Scalable Video Coding
TL;DR: An improved coarse-grain scalable video coding approach based on the structural similarity (SSIM) index as the visual quality criterion, aiming at maximizing the overall coding performance constrained by user-defined quality weightings for all scalable layers is proposed.
Proceedings ArticleDOI
Quality prediction of asymmetrically compressed stereoscopic videos
Jiheng Wang,Shiqi Wang,Zhou Wang +2 more
TL;DR: A binocular rivalry inspired model is used to account for the prediction bias, leading to significantly improved quality estimation of stereoscopic videos and new insight on the development of high efficiency 3D video coding schemes.
Proceedings ArticleDOI
Functional data classification for temporal gene expression data with kernel-induced random forests
TL;DR: The kernel-induced random forest method is extended for discriminating functional data by defining kernel functions of two curves and is demonstrated by classifying the temporal gene expression data.
Journal ArticleDOI
Learning a No-Reference Quality Predictor of Stereoscopic Images by Visual Binocular Properties
TL;DR: A novel no-reference quality assessment metric for stereoscopic images based on monocular and binocular features, motivated by visual perception properties of the human visual system (HVS) named binocular rivalry andbinocular integration is developed.
Journal ArticleDOI
Perceptual Quality Assessment for Asymmetrically Distorted Stereoscopic Video by Temporal Binocular Rivalry
TL;DR: A novel temporal binocular rivalry inspired weighting method is designed to integrate the quality scores of left- and right-views for the final visual quality prediction of SV sequences, which is denoted as the second-stage weighting.