| Chen, Baoliang, Huang, Danni, Zhu, Hanwei, Zhu, Lingyu, Zhou, Wei, Wang, Shiqi, Fang, Yuming and Lin, Weisi 2026. From global to granular: revealing IQA model performance via correlation surface. IEEE Transactions on Pattern Analysis and Machine Intelligence 48 (10) , pp. 13099-13116. 10.1109/tpami.2026.3705184 |
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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 |ΔMOS |). 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 Granularity-Modulated Correlation (GMC), which provides a structured, fine-grained analysis of IQA performance. GMC includes: (1) a 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 Distribution Regulator that regularizes correlations to mitigate biases from non-uniform quality distributions. The resulting correlation surface maps correlation values as a joint function of MOS and |ΔMOS|, 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.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Additional Information: | RRS applied |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 0162-8828 |
| Date of First Compliant Deposit: | 22 September 2026 |
| Last Modified: | 08 Oct 2026 13:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/187957 |
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