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首页> 外文期刊>Journal of neurosurgical sciences >A Very Fast and Accurate Image Quality Assessment Method based on Mean Squared Error with Difference of Gaussians
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A Very Fast and Accurate Image Quality Assessment Method based on Mean Squared Error with Difference of Gaussians

机译：基于Gaussians差异的平均平均误差的一种非常快速准确的图像质量评估方法

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摘要

Mean squared error (MSE) has long been the most useful objective image quality assessment (IQA) metric due to its mathematical tractability and computational simplicity, although it has shown poor correlations with the perceived visual quality for distorted images. Contrary to the MSE, recent IQA methods are more closely related with measured visual quality. However, their applications are somewhat limited due to their heavy computational costs and inapplicability in optimization process. In order to develop a better IQA method that will be closer to the perceived visual quality, the authors aimed to incorporate simple yet powerful linear features into the form of MSE while retaining the advantages of computational simplicity and desirable mathematical properties of MSE. Through comprehensive experiments, the authors found that Difference of Gaussians (DoG) kernel significantly improves the prediction performance while keeping the aforementioned advantages in the form of MSE. The proposed method performs better as the DoG filtering well approximates the behaviors of neural response functions in the visual cortex of the human visual system, thus extracting perceptually important features. At the same time, it holds the computational simplicity and mathematical properties of MSE since DoG is a very simple linear kernel. Their extensive experiments showed that the proposed method provides competitive prediction performance to the recent IQA methods with a significantly lower computational complexity. (C) 2020 Society for Imaging Science and Technology.
机译：由于其数学途径和计算简单，均长的平方误差（MSE）长期以来最有用的客观图像质量评估（IQA）度量，尽管它与对扭曲图像的感知的视觉质量相关的相关性差。与MSE相反，最近的IQA方法与测量的视觉质量更密切相关。然而，由于它们在优化过程中的重量计算成本和不适用性，它们的应用有所限制。为了开发更好的IQA方法，将更接近感知的视觉质量，作者旨在将简单但强大的线性特征纳入MSE的形式，同时保留了MSE的计算简单性和理想的数学特性的优点。通过综合实验，作者发现高斯（狗）内核的差异显着提高了预测性能，同时保持上述优势以MSE的形式。该方法随着狗滤波很好地逼近人类视觉系统的视觉皮质中神经响应函数的行为而更好，从而提取感知的重要特征。与此同时，它拥有MSE的计算简单性和数学属性，因为狗是一个非常简单的线性内核。它们广泛的实验表明，该方法为最近的IQA方法提供了竞争预测性能，具有显着降低的计算复杂性。 （c）2020年影像科技协会。

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