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Image Quality Assessment
Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations.
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Image Quality Assessment
http://sse.tongji.edu.cn/linzhang/IQA/IQA.htm
Reference Image
Image QualityAssessment (IQA) MLA2013
Image Fidelity Metrics
Accumulate physical errors of the image
Mean Squared Error (MSE)Peak Signal to Noise Ratio (PSNR)
IQASignal Structure Metrics
Describe image degradation withperceived change in Structural
HVS Model Metrics
Simulate various aspects of the HVSperception property
Daly visible differences predictor (VDP)Perceptual Distortion Metric (PDM)
Machine Learning g Metrics
Utilize machine learning in differentaspects of image quality assessment
Structure Similarity (SSIM)Feature-Similarity (FSIM)
informationBlind Image Quality Index (BIQI)
Blind/Referenceless Image SpatialQUality Evaluator (BRISQUE)
VIPS Lab, Xidian University
Database Original images Distorted images Types of distortion
LIVEII 29 982 5
TID2008 25 1700 17
MICT 14 168 2
IVC 10 195 4
CSIQ 30 750 5
Evaluation criteria metrics
PLCC: Pearson linear correlation coefficient, provides the prediction accuracy.
SROCC: Spearman rank-order correlation coefficient, measures the predictionmonotonicity.
RMSE: Root mean square error.
MAE: Mean absolute error.
Databases and Evaluation Criteria
Blind Image Quality Assessment
PSNR PSNR PSNR PSNR
http://live.ece.utexas.edu/research/quality/ http://www.umiacs.umd.edu/~pengye/index.html
CVPR 2012,2013,2014
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Blind Image Quality Assessment
NR-IQA
PSNR PSNR PSNR PSNR
Exploit Discriminant Features (transformation domain: wavelete transform, DCT transform)
Linear regression , SVM
Training (Codebook, Dictionary Learing)
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CNN for BLIND IQA
Contributions
1 One of contributions is that they modified the network structure, such that it can learn image quality features more effectively.
2 It proposed a novel framework that allows learning and prediction of image quality on local regions.
3 The language of this paper is very good.
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…Image quality
Input
Blind IQA Using a General Regression Neural Network
OutputLayer
SummationLayer
FeaturesImageDatabase
Layer Patten LayerGeneral Regression Neural
[Li & Bovik, IEEE TNN, 2011]
Network (GRNN)
This quality measure based on several complementary and perceptually relevantimage features fed to a GRNN networkApproximating the functional relationship between these features and subjectivemean opinion scoresExperimental results show the method be closely with human subjective judgment
Similar methods for BLIND IQA
Similar methods for BLIND IQA
Unsupervised Feature Learning for No-reference IQA
Encoding Pooling
Features Image qualityTest Image Soft assignment Support vectorRegression (SVR)
K-means
Codebook Training set
Raw-image-patches are used to learn a codebook via K-means clusteringSoft-assignment coding with max pooling to obtain effective feature representationsImage features are projected to quality scores through support vector regression.
It is a general-purpose no-reference IQA method and can be adapted to differentdomains.
[P. Ye, D. Doermann, et al., CVPR, 2012]
Machine Learning and IQA
Learning without Human Scores for Blind IQA
Develop an effective BIQA without human
Distortion
Patch Extraction
scored images for training
Group patches into different groups, and QAC
is applied to each group to learn the quality-
aware centroids
Compare each patch with the centroids and
assign a score with the weighted average
Patch QualityEstimation Feature Extraction
Mapping
QualityAwareClustering
(QAC)
[Xue & Zhang, CVPR, 2013]
Function
Image Quality
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