SOTAVerified

Full reference image quality assessment

The goal is to calculate an objective quality score for a given (potentially) distorted image given its reference (potentially) pristine quality image is available. Training-free metrics are listed.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 50 papers

TitleStatusHype
Digital twins enable full-reference quality assessment of photoacoustic image reconstructions—0
Perceptual Error Logarithm: An Efficient and Effective Analytical Method for Full-Reference Image Quality Assessment—0
Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality AssumptionCode2
BELE: Blur Equivalent Linearized Estimator—0
Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference—0
Image Quality Assessment: Enhancing Perceptual Exploration and Interpretation with Collaborative Feature Refinement and Hausdorff distance—0
Foundation Models Boost Low-Level Perceptual Similarity MetricsCode0
Sliced Maximal Information Coefficient: A Training-Free Approach for Image Quality Assessment EnhancementCode1
A study of why we need to reassess full reference image quality assessment with medical images—0
Perceptual Quality Assessment for Video Frame Interpolation—0
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All datasetsTID2008ESPLKADID10KDRIQ

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MDSISRCC0.85—Unverified
2SFFSRCC0.83—Unverified
3VSISRCC0.82—Unverified
4VIFSRCC0.81—Unverified
5GMSDSRCC0.78—Unverified
6FSIMcSRCC0.78—Unverified
7SR-SIMSRCC0.76—Unverified
8IW-SSIMSRCC0.69—Unverified
9MADSRCC0.69—Unverified
10MS-SSIMSRCC0.67—Unverified