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Monogenic Signal Theory Based Feature Similarity Index For Image Quality Assessment

机译：基于单信号理论的特征相似度指标用于图像质量评估

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摘要

Image quality assessment (IQA) aims to establish generic metrics consistently with subjective evaluations using computational models. Recent phase congruency, which is a dimensionless, normalized feature of a local structure, is used as the structure similarity feature. This paper proposes a novel feature similarity (RMFSIM) index for full reference IQA based on monogenic signal theory. A monogenic phase congruency map, which is equipped to be relatively insensitive to noise variations, is constructed using phase, orientation and energy information of the 2D monogenic signal. The corresponding 1st-order and 2nd-order coefficients of the MPC map are obtained by Riesz transform. The local feature coefficients similarity is computed by the similarity measure and a single similarity score are combined together finally. Experimental results demonstrate that the proposed similarity index is highly consistent with human subjective evaluations and achieves good performance in terms of prediction monotonicity and accuracy.
机译：图像质量评估（IQA）旨在使用计算模型与主观评估建立一致的通用指标。最近的相位一致性是局部结构的无量纲归一化特征，被用作结构相似性特征。本文提出了一种基于单基因信号理论的全参考IQA的新颖特征相似度（RMFSIM）指标。使用2D单基因信号的相位，方向和能量信息构建单相相位一致性图，该图被配置为对噪声变化相对不敏感。通过Riesz变换获得MPC图的相应的一阶和二阶系数。通过相似度度量计算局部特征系数的相似度，最后将单个相似度分数组合在一起。实验结果表明，所提出的相似性指标与人类的主观评价高度一致，并且在预测单调性和准确性方面取得了良好的性能。

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