activity
20102021
most citeddipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs

325 citations · 427 across the 14 of their papers we have counts for

collaborators

15 papers

eess.IV2021★ 6 cited

Perceptual Quality Assessment of Colored 3D Point Clouds

Honglei Su, Qi Liu, Zhengfang Duanmu +2

The real-world applications of 3D point clouds have been growing rapidly in recent years, but not much effective work has been dedicated to perceptual quality assessment of colored…

eess.IV2021

Degraded Reference Image Quality Assessment

Shahrukh Athar, Zhou Wang

In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely…

eess.IV2021

Deep Image Debanding

Raymond Zhou, Shahrukh Athar, Zhongling Wang +1

Banding or false contour is an annoying visual artifact whose impact is even more pronounced in ultra high definition, high dynamic range, and wide colour gamut visual content, whi…

eess.IV2021★ 7 cited

Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge

Shahrukh Athar, Zhongling Wang, Zhou Wang

The enormous space and diversity of natural images is usually represented by a few small-scale human-rated image quality assessment (IQA) datasets. This casts great challenges to d…

eess.IV2021★ 1 cited

Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical Naturalness

Wei Zhou, Zhou Wang, Zhibo Chen

Single image super-resolution (SISR) algorithms reconstruct high-resolution (HR) images with their low-resolution (LR) counterparts. It is desirable to develop image quality assess…

eess.IV2020★ 1 cited

Learning-Based Quality Assessment for Image Super-Resolution

Tiesong Zhao, Yuting Lin, Yiwen Xu +2

Image Super-Resolution (SR) techniques improve visual quality by enhancing the spatial resolution of images. Quality evaluation metrics play a critical role in comparing and optimi…