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الرئيسية
SOTA
تقييم جودة الفيديو
Video Quality Assessment On Msu Sr Qa Dataset
Video Quality Assessment On Msu Sr Qa Dataset
المقاييس
KLCC
PLCC
SROCC
Type
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
KLCC
PLCC
SROCC
Type
Paper Title
Repository
3SSIM
0.16365
0.20138
0.21450
FR
-
-
ClipIQA+
0.69774
0.71808
0.56875
NR
Exploring CLIP for Assessing the Look and Feel of Images
-
MUSIQ trained on KONIQ
0.51897
0.59151
0.64589
NR
MUSIQ: Multi-scale Image Quality Transformer
-
FSIM
0.26942
0.35083
0.34996
FR
FSIM: A Feature Similarity Index for Image Quality Assessment
-
MSE
0.12067
0.09428
0.16441
FR
-
-
PieAPP
0.61945
0.75743
0.75215
FR
PieAPP: Perceptual Image-Error Assessment through Pairwise Preference
-
Linearity (Norm-in-Norm Loss)
0.52172
0.62204
0.64382
NR
Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment
-
MS-SSIM
0.07821
0.16035
0.11017
FR
Multiscale structural similarity for image quality assessment
TOPIQ (IAA)
0.40663
0.51061
0.51687
NR
TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
-
DBCNN
0.55139
0.63971
0.68621
NR
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
-
Q-Align (IQA)
0.61677
0.74116
0.75088
NR
Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
-
LPIPS (Alex)
0.43158
0.52385
0.54461
FR
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
-
PSNR over Y
0.09998
0.13840
0.12914
FR
-
-
TOPIQ trained on PIPAL
0.42811
0.57564
0.55568
FR
TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
-
LPIPS (VGG)
0.41471
0.52820
0.52868
FR
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
-
ERQA
0.47785
0.60188
0.59345
FR
ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution
-
AHIQ
0.47674
0.62311
0.60468
FR
-
-
VMAF
0.32283
0.40073
0.43219
FR
-
-
DISTS
0.42320
0.55042
0.53346
FR
Image Quality Assessment: Unifying Structure and Texture Similarity
-
Q-Align (IAA)
0.42211
0.50055
0.51521
NR
Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
-
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