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ARXIV:2601.21738 · IMAGE QUALITY ASSESSMENT · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.21738IMAGE QUALITY ASSESSMENTSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A new method, GMC, provides fine-grained analysis of IQA model performance, surpassing traditional metrics with detailed 3D correlation mapping.
Opportunity summary
Pain A new method, GMC, provides fine-grained analysis of IQA model performance, surpassing traditional metrics with detailed 3D correlation mapping.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A new method, GMC, provides fine-grained analysis of IQA model performance, surpassing traditional metrics with detailed 3D correlation mapping. While widely adopted, these metrics reduce performance to a single scalar, failing to capture how…
Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank-Order Correlation Coefficient (SRCC). While widely adopted, these metrics reduce…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. For example, two IQA models may achieve identical SRCC values, yet one ranks high-quality images (related to high Mean Opinion Score, MOS) more reliably,…
Image Quality Assessment moved forward this cycle; last verified April 2026. Public score 5.0/10.
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A new method, GMC, provides fine-grained analysis of IQA model performance, surpassing traditional metrics with detailed 3D correlation mapping.
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10.48550/arXiv.2601.21738A new method, GMC, provides fine-grained analysis of IQA model performance, surpassing traditional metrics with detailed 3D correlation mapping.
Abstract
Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank-Order Correlation Coefficient (SRCC). While widely adopted, these metrics reduce performance to a single scalar, failing to capture how ranking consistency varies across the local quality spectrum. For example, two IQA models may achieve identical SRCC values, yet one ranks high-quality images (related to high Mean Opinion Score, MOS) more reliably, while the other better discriminates image pairs with small quality/MOS differences (related to $|Δ$MOS$|$). Such complementary behaviors are invisible under global metrics. Moreover, SRCC and PLCC are sensitive to test-sample quality distributions, yielding unstable comparisons across test sets. To address these limitations, we propose \textbf{Granularity-Modulated Correlation (GMC)}, which provides a structured, fine-grained analysis of IQA performance. GMC includes: (1) a \textbf{Granularity Modulator} that applies Gaussian-weighted correlations conditioned on absolute MOS values and pairwise MOS differences ($|Δ$MOS$|$) to examine local performance variations, and (2) a \textbf{Distribution Regulator} that regularizes correlations to mitigate biases from non-uniform quality distributions. The resulting \textbf{correlation surface} maps correlation values as a joint function of MOS and $|Δ$MOS$|$, providing a 3D representation of IQA performance. Experiments on standard benchmarks show that GMC reveals performance characteristics invisible to scalar metrics, offering a more informative and reliable paradigm for analyzing, comparing, and deploying IQA models. Codes are available at https://github.com/Dniaaa/GMC.
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PROBLEM
A new method, GMC, provides fine-grained analysis of IQA model performance, surpassing traditional metrics with detailed 3D correlation mapping. While widely adopted, these metrics reduce performance to a single scalar, failing to capture how ranking consistency varies across th...
METHOD
Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank-Order Correlation Coefficient (SRCC). While widely adopted, these metrics reduce performance to a si...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. For example, two IQA models may achieve identical SRCC values, yet one ranks high-quality images (related to high Mean Opinion Score, MOS) more reliably, while the other better discriminates image pairs w...
WHY NOW
Image Quality Assessment moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A new method, GMC, provides fine-grained analysis of IQA model performance, surpassing traditional metrics with detailed 3D correlation mapping. While widely adopted, these metrics reduce performance to a single scalar, failing to capture how ranking consistency varies across the local quality spectrum.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank-Order Correlation Coefficient (SRCC). While widely adopted, these metrics reduce performance to a single scalar, failing to capture how ranking consistency varies across the local quality spectrum.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. For example, two IQA models may achieve identical SRCC values, yet one ranks high-quality images (related to high Mean Opinion Score, MOS) more reliably, while the other better discriminates image pairs with small quality/MOS differences (related to $|Δ$MOS$|$).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Image Quality Assessment moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new method, GMC, provides fine-grained analysis of IQA model performance, surpassing traditional metrics with detailed 3D correlation mapping.
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Image Quality Assessment
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