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Models/DBCNN

DBCNN

Reported on 18 benchmarks across 3 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision12 results

Video UnderstandingonMSU NR VQA Database
KLCC· 2019-07-05
0.775
best: 0.7883 (MDTVSFA)
SOTA
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
Video UnderstandingonMSU NR VQA Database
SRCC· 2019-07-05
0.922
best: 0.9289 (MDTVSFA)
SOTA
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
VideoonMSU NR VQA Database
KLCC· 2019-07-05
0.775
best: 0.7883 (MDTVSFA)
SOTA
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
VideoonMSU NR VQA Database
SRCC· 2019-07-05
0.922
best: 0.9289 (MDTVSFA)
SOTA
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
Video UnderstandingonMSU NR VQA Database
PLCC· 2019-07-05
0.9222
best: 0.9431 (MDTVSFA)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
Video UnderstandingonMSU SR-QA Dataset
KLCC· 2019-07-05
0.55139
best: 0.69774 (ClipIQA+)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
Video UnderstandingonMSU SR-QA Dataset
PLCC· 2019-07-05
0.63971
best: 0.75743 (PieAPP)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
Video UnderstandingonMSU SR-QA Dataset
SROCC· 2019-07-05
0.68621
best: 0.75215 (PieAPP)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
VideoonMSU NR VQA Database
PLCC· 2019-07-05
0.9222
best: 0.9431 (MDTVSFA)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
VideoonMSU SR-QA Dataset
KLCC· 2019-07-05
0.55139
best: 0.69774 (ClipIQA+)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
VideoonMSU SR-QA Dataset
PLCC· 2019-07-05
0.63971
best: 0.75743 (PieAPP)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
VideoonMSU SR-QA Dataset
SROCC· 2019-07-05
0.68621
best: 0.75215 (PieAPP)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665

Time Series6 results

Video Quality AssessmentonMSU NR VQA Database
KLCC· 2019-07-05
0.775
best: 0.7883 (MDTVSFA)
SOTA
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
Video Quality AssessmentonMSU NR VQA Database
SRCC· 2019-07-05
0.922
best: 0.9289 (MDTVSFA)
SOTA
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
Video Quality AssessmentonMSU NR VQA Database
PLCC· 2019-07-05
0.9222
best: 0.9431 (MDTVSFA)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
Video Quality AssessmentonMSU SR-QA Dataset
KLCC· 2019-07-05
0.55139
best: 0.69774 (ClipIQA+)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
Video Quality AssessmentonMSU SR-QA Dataset
PLCC· 2019-07-05
0.63971
best: 0.75743 (PieAPP)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665
Video Quality AssessmentonMSU SR-QA Dataset
SROCC· 2019-07-05
0.68621
best: 0.75215 (PieAPP)
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network arXiv:1907.02665