Biologically inspired image quality assessment
Biologically inspired image quality assessment
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DOI：
10.1016/j.sigpro.2015.08.012
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
Fei Gao;Jun Yu
中科院分区：
文献类型：
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作者：
Fei Gao;Jun Yu
图像质量评估（IQA）旨在开发能够精确、自动地估计人类感知图像质量的计算模型。迄今为止，已经提出了各种IQA方法来模拟人类视觉系统的处理，但收效甚微。在这里，我们提出了一种新的IQA方法，称为生物启发特征相似性（BIFS），该方法被证明与人类感知高度一致。在该方法中，首先提取测试图像和相关参考图像的生物启发特征（bif）。然后，计算参考bif与失真bif之间的局部相似度，并将其组合得到最终的质量指标。在多个IQA数据库上进行的深入实验表明，所提出的方法是非常有效和稳健的，并且在各种数据集上优于最先进的FR-IQA方法。
Image quality assessment (IQA) aims at developing computational models that can precisely and automatically estimate human perceived image quality. To date, various IQA methods have been proposed to mimic the processing of the human visual system, with limited success. Here, we present a novel IQA approach named biologically inspired feature similarity (BIFS), which is demonstrated to be highly consistent with the human perception. In the proposed approach, biologically inspired features (BIFs) of the test image and the relevant reference image are first extracted. Afterwards, local similarities between the reference BIFs and the distorted ones are calculated and then combined to obtain a final quality index. Thorough experiments on a number of IQA databases demonstrate that the proposed method is highly effective and robust, and outperform state-of-the-art FR-IQA methods across various datasets.