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20192026
most citedBlind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network

938 citations · 1.4k across the 17 of their papers we have counts for

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eess.IV202151 cited

Locally Adaptive Structure and Texture Similarity for Image Quality Assessment

Keyan Ding, Yi Liu, Xueyi Zou +2

The latest advances in full-reference image quality assessment (IQA) involve unifying structure and texture similarity based on deep representations. The resulting Deep Image Struc…

eess.IV2021

Debiased Subjective Assessment of Real-World Image Enhancement

Cao Peibei, Wang Zhangyang, Ma Kede

In real-world image enhancement, it is often challenging (if not impossible) to acquire ground-truth data, preventing the adoption of distance metrics for objective quality assessm…

eess.IV2020

Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos

Xiangjie Sui, Kede Ma, Yiru Yao +1

Omnidirectional images (also referred to as static 360° panoramas) impose viewing conditions much different from those of regular 2D images. How do humans perceive image distortion…

eess.IV2020

Comparison of Image Quality Models for Optimization of Image Processing Systems

Keyan Ding, Kede Ma, Shiqi Wang +1

The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments. Perceptual datasets gathe…

eess.IV2020

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…

eess.IV2019938 cited

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network

Weixia Zhang, Kede Ma, Jia Yan +2

We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions. Our model consists of two convolutional neural net…