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.

翻译：长期以来，图像质量评估（IQA）模型的评估一直由全局相关性指标主导，例如皮尔逊线性相关系数（PLCC）和斯皮尔曼秩相关系数（SRCC）。尽管这些指标被广泛采用，但它们将性能简化为单一标量，无法捕捉排序一致性在局部质量谱上的变化。例如，两个IQA模型可能获得相同的SRCC值，但其中一个对高质量图像（与高平均意见得分MOS相关）的排序更可靠，而另一个则能更好地区分质量/MOS差异较小（与$|Δ$MOS$|$相关）的图像对。这种互补行为在全局指标下是不可见的。此外，SRCC和PLCC对测试样本的质量分布敏感，导致跨测试集的比较不稳定。为了解决这些局限性，我们提出了**细粒度调制相关性（GMC）**，为IQA性能提供结构化、细粒度的分析。GMC包括：（1）一个**细粒度调制器**，它根据绝对MOS值和成对MOS差异（$|Δ$MOS$|$）应用高斯加权相关性，以检验局部性能变化；（2）一个**分布调节器**，用于正则化相关性以减轻非均匀质量分布带来的偏差。由此产生的**相关性曲面**将相关值映射为MOS和$|Δ$MOS$|$的联合函数，提供了IQA性能的三维表示。在标准基准测试上的实验表明，GMC揭示了标量指标无法观测到的性能特征，为分析、比较和部署IQA模型提供了一个信息更丰富、更可靠的范式。代码可在 https://github.com/Dniaaa/GMC 获取。