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Image Quality Assessment

Papers

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Showing 476–500 of 732 papers

TitleStatusHype
A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography ImageCode1
Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network—0
Image restoration quality assessment based on regional differential information entropy—0
Semi-Supervised Deep Ensembles for Blind Image Quality AssessmentCode0
An ensemble multi-scale residual attention network (EMRA-net) for image DehazingCode0
Estimating MRI Image Quality via Image Reconstruction Uncertainty—0
Deep Perceptual Preprocessing for Video Coding—0
A Multi-task convolutional neural network for blind stereoscopic image quality assessment using naturalness analysisCode1
Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment—0
Evaluating Foveated Video Quality Using Entropic Differencing—0
Evaluating Deep Neural Networks for Image Document Enhancement—0
Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment—0
Design of Low-Artifact Interpolation Kernels by Means of Computer AlgebraCode0
Consumer Image Quality Prediction using Recurrent Neural Networks for Spatial PoolingCode0
Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessment—0
Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical NaturalnessCode1
Troubleshooting Blind Image Quality Models in the Wild—0
NTIRE 2021 Challenge on Perceptual Image Quality Assessment—0
ER-IQA: Boosting Perceptual Quality Assessment Using External Reference Images—0
(ASNA) An Attention-based Siamese-Difference Neural Network with Surrogate Ranking Loss function for Perceptual Image Quality AssessmentCode1
Perceptual Image Quality Assessment with TransformersCode1
Improving precision of objective image/video quality metrics—0
Region-Adaptive Deformable Network for Image Quality AssessmentCode1
Generalizable No-Reference Image Quality Assessment via Deep Meta-learningCode1
On the Applicability of Synthetic Data for Face Recognition—0
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All datasetsMSU NR VQA DatabaseKonIQ-10kMSU FR VQA Database

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SPAQ MT-SSRCC0.88—Unverified
2SPAQ BLSRCC0.88—Unverified
3SPAQ MT-ASRCC0.88—Unverified
4KonCept512SRCC0.84—Unverified
5MEONSRCC0.51—Unverified
#ModelMetricClaimedVerifiedStatus
1RealQASRCC0.95—Unverified
2OneAlignSRCC0.94—Unverified
3UNIQASRCC0.93—Unverified
4KonCept512SRCC0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1FSIMSRCC0.9—Unverified
2DSSSRCC0.9—Unverified
3MS-GMSDSRCC0.89—Unverified
4GMSDSRCC0.89—Unverified