图像质量评价和视频质量评价(IQA/VQA)

IQA/VQA(image quality assessment/video quality assessment)

1.FR(Full Reference)
2.RR(Reduced Reference)
3.NR(No Reference)

datasets:LIVE/CSIQ/TIB2013 etc...

distortions

1.block artifacts(deblocking filter)
2.ringing effect
3.mosquito noise
4.blur
etc...

subjective methods

1.MOS(Mean Opinion Score)
    Single Stimulus Methods
2.DMOS(Differential Mean Opinion Score)
    Double Stimulus Methods
    

objective methods

evaluation metrics

1.LCC(Linear Correlation Coefficient/Pearson Correlation Coefficient)
2.SROCC(Spearman Rank Order Correlation Coefficient )
3.KROCC(Kendall Rank Order Correlation Coefficient)
4.RMSE(Root Mean Square Error)
5.OR(Outlier ratio)

FR

1.MSE
2.PSNR
3.SSIM,MS-SSIM
4.VIF(visual information fidelity)
5.JND(Just Noticeable Difference)
6.VMAF(Visual Multimethod Assessment Fusion)

NR(blind image quality assessment)

traditional

1.BRISQUE
    paper:No-Reference Image Quality Assessmentin the Spatial Domain
    ideas:
        1.MSCN(mean subtracted contrast normalized coefficients)
        2.NSS(natural scene statistics):GGD(generalized Gaussian distribution),
                AGGD(asymmetric generalized Gaussian distribution)
        3.GGD,AGGD parameters estimation,concat feature vector,train SVM


2.NIQE
    paper:Making a ‘Completely Blind’ Image Quality Analyzer
    ideas:
        1.opinion unware
        2.patch selection:The variance field
        3.MGD(Multivariate Gaussian distribution):directly calculate score


3.BIQI
    paper:A Two-Step Framework for Constructing Blind Image Quality Indices
    ideas:
        1.estimates the presence of a set of distortions in the image
        2.evaluates the quality of the image along each of these distortions


4.VIIDEO(for video)
    paper:A Completely Blind Video Integrity Oracle
    ideas:
        1.Spatial Domain Natural Video Statistics: analyse local statistics of frame
            differences  of videos
        2.Compute low pass filtered frame difference coefficients


5.DIIVINE
    paper:Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
    ideas:
        1.2-stage framework involving distortion identification followed by
               distortion-specific quality assessment
        2.Statistical Model for Wavelet Coefficients


6.BLINDS-II:
    paper:
    ideas:
        1.DCT domain:block DCT coefficients(estimate GGD parameters)
        2.a simple Bayesian inference model to predict image quality scores
  

deep learning

1.Le Kang 2014
    paper:Convolutional Neural Networks for No-Reference Image Quality Assessment
    ideas:
        1.Taking image patches as input, the CNN works in the spatial domain without using
            hand-crafted features that are employed by most previous methods. 

2.DIQI
    paper:Deep Learning Network For Blind Image Quality Assessment
    ideas:
        1.RGB2YIQ
        2.sparse autoencoder is adopted to pre-train each layer(L-BFGS)
        3.fine-tune the DNN

3.DIQA:
    paper:Deep CNN-Based Blind Image Quality Predictor
    ideas:
        1.in objective distortion part, a pixelwise objective error map is predicted
            using the CNN model.
        2.in HVS-related part, model further learns the human visual perception behavior.

4.DeepBIQ
    paper:On the Use of Deep Learning for Blind Image Quality Assessment
    ideas:
        1.estimates the image quality by average-pooling the scores predicted on multiple
            sub-regions of the original image
        2.fine-tuned for category-based image quality assessment.


5.RankIQA:
    paper:RankIQA: Learning from Rankings for No-reference Image Quality Assessment
    ideas:
        1.Siamese Network
        2.rank score

6.WaDIQaM-FR/NR
    paper:Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
    ideas:
        1.Patch weight estimate&Patch quality estimate
        

references

Laboratory for Image & Video Engineering
blind image quality tool box
tensorflow2 DIQA
BRISQUE opencv3