Quality Assessment Paper List（研二）

从2018年5月到2019年5月，大概一年（研二）的时间里学习的有关quality assessment的论文。（共128篇）

<PS：红色标注为经典论文>

相关代码可以在以下百度云盘链接里查找，代码均来源于原论文作者主页：

链接：https://pan.baidu.com/s/1i485TWhSEh-DL3QVtV1gRA 提取码：9sfr

Image数据库：

LIVE CSIQ TID BID CID CLIVE KONIQ10K AVA...

Analysis of Public Image and Video Databases for Quality Assessment. 2012 JSTSP

Waterloo Exploration Database New Challenges for Image Quality Assessment Models. 2017 IEEE TIP

Video数据库：

Study of Subjective and Objective Quality Assessment of Video. 2010 IEEE TIP

CVD2014 A Database for Evaluating No-reference Video Quality Assessment Algorithms. 2016 IEEE TIP

The Konstanz Natural Video Database. 2017 QoMEX

Large Scale Study of Perceptual Video Quality. 2018 IEEE CVPR

Image Quality Assessment：

1. 传统方法

【SSIM】【FR】Image Quality Assessment From Error Visibility to Structural Similarity. 2004 IEEE TIP

【MS-SSIM】【FR】Multi-scale Structural Similarity for Image Quality Assessment. 2003

【FSIM】【FR】FSIM A Feature Similarity Index for Image Quality Assessment. 2011 IEEE TIP

【IW-SSIM】【FR】Information Content Weighting for Perceptual Image Quality Assessment. 2011 IEEE TIP

【GSIM】【FR】Image Quality Assessment based on Gradient Similarity. 2012 IEEE TIP

【RFSIM】【FR】RFSIM A Feature based Image Quality Assessment Metric Using RIESZ Transforms. 2010 IEEE ICIP

【SRSIM】【FR】SR-SIM A Fast and High Performance IQA Index based on Spectral Residual. 2012 IEEE ICIP

【SPSIM】【FR】SPSIM A Superpixel-based Similarity Index for Full-reference Image Quality Assessment. 2018 IEEE TIP

【MDSI】Mean Deviation Similarity Index Efficient and Reliable Full-reference Image Quality Evaluator. 2016 IEEE Access

【GMSD】【FR】Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index. 2014 IEEE TIP

【VSI】【FR】 VSI A Visual Saliency-induced Index for Perceptual Image Quality Assessment. 2014 IEEE TIP

【VIF】【FR】Image Information and Visual Quality. 2006 IEEE TIP

【IFC】【FR】An Information Fidelity Criterion for Image Quality Assessment Using Natural Scene Statistics. 2005 IEEE TIP

【VSNR】【FR】VSNR A Wavelet-based Visual Signal-to-Noise Ratio for Natural Images. 2007 IEEE TIP

【MAD】【FR】Most Apparent Distortion Full-reference Image Quality Assessment and the Role of Strategy. 2010 JEI

【RRED】【RR】RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment. 2012 IEEE TIP

【QAC】【NR】Learning without Human Scores for Blind Image Quality Assessment. 2013 IEEE CVPR

【NQM】【NR】Image Quality Assessment based on a Degradation Model. 2000 IEEE TIP

【NIQE】【NR】Making a “Completely Blind” Image Quality Analyzer. 2013 IEEE SPL

【BRISQUE】【NR】No-reference Image Quality Assessment in the Spatial Domain. 2012 IEEE TIP

【CORNIA】【NR】Unsupervised Feature Learning Framework for No-reference Image Quality Assessment. 2012 IEEE CVPR

【ILNIQE】【NR】A Feature-enriched Completely Blind Image Quality Evaluator. 2015 IEEE TIP

【BLISS】Beyond Human Opinion Scores: Blind Image Quality Assessment based on Synthetic Scores. 2014 IEEE CVPR

【FRIQUEE】Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach. 2016 JOV

A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms. 2006 IEEE TIP

Learning a Blind Measure of Perceptual Image Quality. 2011 IEEE CVPR

A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment. 2018 IEEE TIP代码

Predicting Encoded Picture Quality in Two Steps in a Better Way. 2018 Arxiv

Stereoscopic Image Quality Assessment by Deep Convolutional Neural Network. 2018 JVCIR Yan

Learning a No-reference Quality Predictor of Stereoscopic Images by Visual Binocular Properties. 2018 peprint Yan

Quality Assessment of Asymmetric Stereo Pair Formed from Decoded and Synthesized Views. 2012 QoMEX

A Detail-based Method for Linear Full Reference Image Quality Prediction. IEEE TIP 2018

2. deep-based

【BIQI】A Two-step Framework for Constructing Blind Image Quality Indices. 2010 IEEE SPL

【CNN】Convolutional Neural Networks for No-reference Image Quality Assessment. 2014 IEEE CVPR代码

【GRNN】Blind Image Quality Assessment Using a General Regression Neural Network. 2011 IEEE TNN代码

【DLIQA】Blind Image Quality Assessment via Deep Learning. 2015 IEEE TNN

【deepiqa】 A Deep Neural Network for Image Quality Assessment. 2016 ICIP

【DeepQA】Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework. 2017 IEEE CVPR代码

【deepIQA】Deep Neural Networks for No-reference and Full-reference Image Quality Assessment. 2017 IEEE TIP

【dipIQ】Blind Image Quality Assessment by Learning-to-rank Discriminable Image Pairs. 2017 IEEE TIP kede

【MEON】End-to-end Blind Image Quality Assessment Using Deep Neural Networks. 2018 IEEE TIP kede代码

Group MAD Competition A New Methodology to Compare Objective Image Quality Models. 2016 IEEE CVPR/2019 PAMI kede

Deep Bilinear Pooling for Blind Image Quality Assessment. 2019 IEEE CSVT kede代码

NIMA Neural Image Assessment. 2018 IEEE TIP

Learning to Rank for Blind Image Quality Assessment. 2015 IEEE TNN

【BPSQM】Blind Predicting Similar Quality Map for Image Quality Assessment. 2018 IEEE CVPR

【BIECON】Full Deep Blind Image Quality Predictor. 2017 JSTSP

【DIQA】Deep CNN-based Blind Image Quality Predictor. 2018 IEEE TNN

Deep Convolutional Neural Models for Picture-quality Prediction. 2017 IEEE Signal Processing Magazine

Blind Image Quality Assessment via Vector Regression and Object Oriented Pooling. 2017 IEEE TMM

Image Quality Assessment Guided Deep Neural Networks Training. 2017 Arxiv

A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction. 2017 Arxiv

【MGDNN】Difference of Gaussian Statistical Features based Blind Image Quality Assessment A Deep Learning Approach. 2015 IEEE ICIP

Learning Deep Vector Regression Model for No-reference Image Quality Assessment. 2017 IEEE ICASSP

An Accurate Deep Convolutional Neural Networks Model for No-reference Image Quality Assessment. 2017 IEEE ICME

Blind Proposal Quality Assessment via Deep Objectness Representation and Local Linear Regression. 2017 IEEE ICME

RankIQA Learning from Rankings for No-reference Image Quality Assessment. 2017 IEEE ICCV代码

PieAPP: Perceptual Image-error Assessment through Pairwise Preference. 2018 IEEE CVPR代码

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. 2018 IEEE CVPR代码

Hallucinated-IQA No-reference Image Quality Assessment via Adversarial Learning. 2018 IEEE CVPR代码

An Attention-driven Approach of No-reference Image Quality Assessment. 2017 Arxiv

Deep Multi-patch Aggregation Network for Image Style, Aesthetics and Quality Estimation. 2015 IEEE ICCV

SOM: Semantic Obviousness Metric for Image Quality Assessment. 2015 IEEE CVPR

Deep HVS-IQA Net: Human Visual System Inspired Deep Image Quality Assessment Networks. Arxiv

Learning to Compare Image Patches via Convolutional Neural Networks. 2015 IEEE CVPR

PATCH-IQ A Patch based Learning Framework for Blind Image Quality Assessment. 2017 Information Science

Generating Image Distortion Maps Using Convolutional Autoencoders with Application to No Reference Image Quality Assessment. 2018 IEEE SPL代码

VeNICE: A Very Deep Neural Network Approach to No-reference Image Assessment.

Blind Deep S3D Image Quality Evaluation via Local to Global Feature Aggregation. 2017 IEEE TIP

On the Use of Deep Learning for Blind Image Quality Assessment.

Pairwise Comparison and Rank Learning for Image Quality Assessment. 2016 Displays

Learning to Blindly Assess Image Quality in the Laboratory and Wild. 2019 preprint

Video Quality Assessment：

1. 传统方法

【FR】A Perception-based Hybrid Model for Video Quality Assessment. 2016 IEEE CSVT

【FR】An Optical Flow-based Full Reference Video Quality Assessment Algorithm. 2016 IEEE TIP

【FR】Objective Video Quality Assessment based on Perceptually Weighted Mean Squared Error. 2017 IEEE CSVT

【FR】Spatiotemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment. 2018 IEEE CSVT

【3D-SSIM】【FR】3D-SSIM for Video Quality Assessment. 2012 IEEE ICIP

【MOVIE】【FR】Motion Tuned Spatio-temporal Quality Assessment of Natural Videos. 2010 IEEE TIP

【STMAD】【FR】A Spatiotemporal Most-apparent-distortion Model for Video Quality Assessment. 2011 IEEE ICIP

【V-SSIM】【FR】Video Quality Assessment based on Structural Distortion Measurement. 2004 SPIC

【VQM】【RR】A New Standardized Method for Objectively Measuring Video Quality. 2004 IEEE TOB

【STRRED】【RR】Video Quality Assessment by Reduced Reference Spatio-temporal Entropic Differencing. 2013 IEEE CSVT

【VIIDEO】【NR】A Completely Blind Video Integrity Oracle. 2016 IEEE TIP

【BLIINDS】【NR】Blind Prediction of Natural Video Quality. 2014 IEEE TIP

【V-CORNIA】【NR】No-reference Video Quality Assessment via Feature Learning. 2015 IEEE ICIP

Spatiotemporal Statistics for Video Quality Assessment. 2016 IEEE TIP

Spatiotemporal Feature Combination Model for No-reference Video Quality Assessment. 2018 QoMEX

Flicker Sensitive Motion Tuned Video Quality Assessment. 2016 SSIAI

Free-energy Principle Inspired Video Quality Metric and Its Use in Video Coding. 2016 IEEE TMM

Study of Saliency in Objective Video Quality Assessment. 2017 IEEE TIP

SpEED-QA: Spatial Efficient Entropic Differencing for Image and Video Quality. 2017 IEEE SPL

Video Quality Pooling Adaptive to Perceptual Distortion Severity. 2013 IEEE TIP

Detecting and Mapping Video Impairments. preprint

A Fast Stereo Video Quality Assessment Method based on Compression Distortion. preprint

2. deep-based

【V-MEON】End-to-End Blind Quality Assessment of Compressed Video Using Deep Neural Networks. 2018 ACM MM 代码

No-reference Video Quality Assessment with 3D Shearlet Transform and Convolutional Neural Networks. 2016 IEEE CSVT

【DeepVQA】Deep Video Quality Assessor From Spatio-temporal Visual Sensitivity to A Convolutional Neural Aggregation Network. 2018 ECCV

Viewport-based CNN: A Multi-task Approach for Assessing 360 Video Quality. preprint

Related paper:

【NLPD】 Perceptually Optimized Image Rendering. 2017 arXiv

Learning Spatiotemporal Features with 3D Convolutional Networks. 2015 IEEE ICCV

Large-scale Video Classification with Convolutional Neural Networks. 2014 IEEE CVPR

3D Convolutional Neural Networks for Human Action Recognition. 2013 IEEE PAMI

Interpretable Convolutional Neural Networks. 2018 arXiv

Rethinking ImageNet Pre-training. 2018 ArXiv

Repr: Improved Training of Convolutional Filters.

UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-level Vision using Diverse Datasets and Limited Memory. 2017 IEEE CVPR

Kervolutional Neural Networks. Arxiv

Network in Network. 2014 ICLR

Deformable Convolutional Networks. 2017 Arxiv

Deep Unsupervised Saliency Detection A Multiple Noisy Labeling Perspective. 2018 ArXiv

Learning without Forgetting. 2018 IEEE PAMI

Learning from Crowds. 2010 Machine Learning Research

Supervised Learning from Multiple Experts Whom to trust when everyone lies a bit. 2009 ICML

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? 2017 NIPS

Quantifying Uncertainties in Natural Language Processing Task. 2018 ArXiv

Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision A Comparative Study. 2012 IEEE PAMI

Geometry-consistent Adversarial Networks for One-sided Unsupervised Domain Mapping. 2018 ArXiv

Unsupervised Deep Embedding for Clustering Analysis. 2016 ICML

A New Three-step Search Algorithm for Block Motion Estimation. 1994 IEEE CSVT

Spatial Frequency, Phase, and the Contrast of Natural Images. 2002

Community-aware Retargeting by Probabilistic Encoding of Noise-tolerant Deep Features. preprint

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