Sparse Feature Fidelity for Perceptual Image Quality Assessment
Sparse Feature Fidelity for Perceptual Image Quality Assessment
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DOI：
10.1109/tip.2013.2266579
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发表时间：
2013-10-01
影响因子：
10.6
通讯作者：
Wang, Ming-Hui
中科院分区：
文献类型：
--
作者：
Chang, Hua-Wen;Yang, Hua;Wang, Ming-Hui
图像质量度量（IQM）的预测应与人类主观评价一致。由于人类视觉系统（HVS）对于视觉感知至关重要，因此 HVS 建模被认为是实现感知质量预测的最合适方法。相当于独立成分分析（ICA）的稀疏编码可以很好地描述初级视觉皮层中简单细胞的感受野，初级视觉皮层是 HVS 最重要的部分。受此启发，在将图像转换为初级视觉皮层稀疏表示的基础上，提出了一种称为稀疏特征保真度（SFF）的质量度量，用于全参考图像质量评估（IQA）。该方法基于特征检测器获取的稀疏特征，该特征检测器通过 ICA 算法对自然图像样本进行训练。此外，还设计了两种策略来模拟视觉感知的特性：1）视觉注意力和2）视觉阈值。 SFF的计算有两个阶段：训练和保真度计算，此外，保真度计算由两个部分组成：特征相似度和亮度相关性。特征相似性衡量两个图像之间的结构差异，而亮度相关性评估亮度失真。 SFF还反映了HVS的色彩特性，对于色彩IQA非常有效。在五个图像数据库上的实验结果表明，与领先的 IQM 相比，SFF 在匹配主观评分方面具有更好的性能。
The prediction of an image quality metric (IQM) should be consistent with subjective human evaluation. As the human visual system (HVS) is critical to visual perception, modeling of the HVS is regarded as the most suitable way to achieve perceptual quality predictions. Sparse coding that is equivalent to independent component analysis (ICA) can provide a very good description of the receptive fields of simple cells in the primary visual cortex, which is the most important part of the HVS. With this inspiration, a quality metric called sparse feature fidelity (SFF) is proposed for full-reference image quality assessment (IQA) on the basis of transformation of images into sparse representations in the primary visual cortex. The proposed method is based on the sparse features that are acquired by a feature detector, which is trained on samples of natural images by an ICA algorithm. In addition, two strategies are designed to simulate the properties of the visual perception: 1) visual attention and 2) visual threshold. The computation of SFF has two stages: training and fidelity computation, in addition, the fidelity computation consists of two components: feature similarity and luminance correlation. The feature similarity measures the structure differences between the two images, whereas the luminance correlation evaluates brightness distortions. SFF also reflects the chromatic properties of the HVS, and it is very effective for color IQA. The experimental results on five image databases show that SFF has a better performance in matching subjective ratings compared with the leading IQMs.