FSIM: A Feature Similarity Index for Image Quality Assessment
FSIM: A Feature Similarity Index for Image Quality Assessment
复制标题
FSIM：图像质量评估的特征相似度指数
DOI：
10.1109/tip.2011.2109730
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发表时间：
2011-08-01
影响因子：
10.6
通讯作者：
Zhang, David
中科院分区：
文献类型：
--
作者：
Zhang, Lin;Zhang, Lei;Zhang, David
图像质量评估（IQA）的目的是使用计算模型来衡量图像质量与主观评价一致。著名的结构相似性指数将IQA从基于像素的阶段带到了基于结构的阶段。基于人类视觉系统（HVS）主要根据图像的低层特征来理解图像这一事实，提出了一种新的特征相似性（FSIM）指标。具体而言，相位一致性（PC），这是一个无量纲的本地结构的重要性的措施，被用作FSIM的主要特征。考虑到PC具有对比度不变性，而对比度信息确实会影响HVS对图像质量的感知，因此采用图像梯度幅值（GM）作为FSIM的辅助特征。PC和GM在表征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作为加权函数来导出单个质量分数。在六个基准IQA数据库上进行的大量实验表明，FSIM可以实现比最先进的IQA指标更高的一致性与主观评价。
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.