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A Highly Efficient Blind Image Quality Assessment Metric of 3-D Synthesized Images Using Outlier Detection | IEEE Journals & Magazine | IEEE Xplore

A Highly Efficient Blind Image Quality Assessment Metric of 3-D Synthesized Images Using Outlier Detection


Abstract:

With multitudes of image processing applications, image quality assessment (IQA) has become a prerequisite for obtaining maximally distinctive statistics from images. Des...Show More

Abstract:

With multitudes of image processing applications, image quality assessment (IQA) has become a prerequisite 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 viewpoint 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.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 15, Issue: 7, July 2019)
Page(s): 4120 - 4128
Date of Publication: 20 December 2018

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