IEEE Trans Image Process. 2011 Aug;20(8):2378-86. doi: 10.1109/TIP.2011.2109730. Epub 2011 Jan 31.
Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.
图像质量评估（IQA）旨在使用计算模型来测量图像质量，使其与主观评估一致。著名的结构相似性指数将 IQA 从基于像素的阶段提升到基于结构的阶段。在本文中，我们提出了一种新的基于全参考 IQA 的特征相似性（FSIM）指数，其依据是人类视觉系统（HVS）主要根据图像的底层特征来理解图像。具体来说，相位一致性（PC）作为 FSIM 的主要特征，它是衡量局部结构重要性的无量纲度量。考虑到 PC 是对比度不变的，而对比度信息确实会影响 HVS 对图像质量的感知，因此在 FSIM 中我们使用图像梯度幅度（GM）作为次要特征。PC 和 GM 在描述图像局部质量方面起着互补的作用。在获得局部质量图之后，我们再次使用 PC 作为加权函数来得出单个质量分数。在六个基准 IQA 数据库上进行的广泛实验表明，FSIM 可以比最先进的 IQA 指标更一致地与主观评估结果相匹配。
