activity
20172019
most citedPrediction of Satisfied User Ratio for Compressed Video

3 citations · 3 across the 1 of their papers we have counts for

collaborators

5 papers

eess.IV2019

C3DVQA: Full-Reference Video Quality Assessment with 3D Convolutional Neural Network

Munan Xu, Junming Chen, Haiqiang Wang +3

Traditional video quality assessment (VQA) methods evaluate localized picture quality and video score is predicted by temporally aggregating frame scores. However, video quality ex…

cs.MM2018

A user model for JND-based video quality assessment: theory and applications

Haiqiang Wang, Ioannis Katsavounidis, Xinfeng Zhang +2

The video quality assessment (VQA) technology has attracted a lot of attention in recent years due to an increasing demand of video streaming services. Existing VQA methods are des…

cs.MM2018

A JND-based Video Quality Assessment Model and Its Application

Haiqiang Wang, Xinfeng Zhang, Chao Yang +1

Based on the Just-Noticeable-Difference (JND) criterion, a subjective video quality assessment (VQA) dataset, called the VideoSet, was constructed recently. In this work, we propos…

cs.MM2018

Analysis and prediction of JND-based video quality model

Haiqiang Wang, Xinfeng Zhang, Chao Yang +1

The just-noticeable-difference (JND) visual perception property has received much attention in characterizing human subjective viewing experience of compressed video. In this work,…

cs.MM20173 cited

Prediction of Satisfied User Ratio for Compressed Video

Haiqiang Wang, Ioannis Katsavounidis, Qin Huang +2

A large-scale video quality dataset called the VideoSet has been constructed recently to measure human subjective experience of H.264 coded video in terms of the just-noticeable-di…