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首页> 外文期刊>IEEE transactions on industrial informatics >A Highly Efficient Blind Image Quality Assessment Metric of 3-D Synthesized Images Using Outlier Detection
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A Highly Efficient Blind Image Quality Assessment Metric of 3-D Synthesized Images Using Outlier Detection

机译：使用离群值检测的3D合成图像的高效盲图像质量评估指标

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摘要

With multitudes of image processing applications, image quality assessment (IQA) has become a pre-requisite for obtaining maximally distinctive statistics from images. Despite the widespread research in this domain over several years, existing IQA algorithms have a number of key limitations concerning different image distortion types and algorithms' computational efficiency. Images that are synthesized using depth image-based rendering have applications in various disciplines, such as free view-point videos, which enable synthesis of novel realistic images in the referenceless environment. In the literature, very few no-reference (NR) quality assessment metrics of three-dimensional (3-D) synthesized images are proposed, and most of them are computationally expensive, which makes it difficult for them to be deployed in real-time applications. In this paper, we attribute the geometrically distorted pixels as outliers in 3-D synthesized images. This assumption is validated using the three sigma rule-based robust outlyingness ratio. We propose a novel fast and accurate blind IQA metric of 3-D synthesized images using nonlinear median filtering since the median filtering has the capability of identifying and removing outliers. The advantages of the proposed algorithm are twofold. First, it uses a simple technique, i. e., median filtering, to capture the level of geometric and structural distortions (up to some extend). Second, the proposed algorithm has higher computational efficiency. Experiments show the superiority of the proposed NR IQA algorithm over existing state-of-the-art full-, reduced-, and NR IQA methods, in terms of both predicting accuracy and computational complexity.
机译：随着大量的图像处理应用程序，图像质量评估（IQA）已成为从图像中获得最大区别统计信息的先决条件。尽管多年来在该领域进行了广泛的研究，但是现有的IQA算法在有关不同图像失真类型和算法的计算效率方面存在许多关键限制。使用基于深度图像的渲染合成的图像在各种学科中都有应用，例如免费视点视频，这些视频可以在无参考的环境中合成新颖逼真的图像。在文献中，提出了很少的三维（3-D）合成图像的无参考（NR）质量评估指标，并且它们中的大多数计算量很大，这使得它们难以实时部署。应用程序。在本文中，我们将几何变形像素归因于3D合成图像中的离群值。使用基于三个西格玛规则的稳健离群率验证了此假设。由于中值滤波具有识别和消除异常值的能力，因此我们提出了使用非线性中值滤波的3-D合成图像的新型快速，准确的盲IQA度量。该算法的优点是双重的。首先，它使用一种简单的技术，即例如，中值滤波，以捕获几何和结构变形的水平（最大程度延伸）。其次，该算法具有较高的计算效率。实验表明，在预测准确性和计算复杂性方面，所提出的NR IQA算法相对于现有的最新的完全，简化和NR IQA方法具有优越性。

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