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首页> 外文期刊>Circuits and Systems for Video Technology, IEEE Transactions on >No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement
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No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement

机译:基于空间角度测量的无参考光场图像质量评估

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摘要

Light field image quality assessment (LFI-QA) is a significant and challenging research problem. It helps to better guide light field acquisition, processing and applications. However, only a few objective models have been proposed and none of them completely consider intrinsic factors affecting the LFI quality. In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency. We first measure the spatial quality deterioration by capturing the naturalness distribution of the light field cyclopean image array, which is formed when human observes the LFI. Then, as a transformed representation of LFI, the Epipolar Plane Image (EPI) contains the slopes of lines and involves the angular information. Therefore, EPI is utilized to extract the global and local features from LFI to measure angular consistency degradation. Specifically, the distribution of gradient direction map of EPI is proposed to measure the global angular consistency distortion in the LFI. We further propose the weighted local binary pattern to capture the characteristics of local angular consistency degradation. Extensive experimental results on four publicly available LFI quality datasets demonstrate that the proposed method outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms.
机译:轻场图像质量评估(LFI-QA)是一个重要而充满挑战性的研究问题。它有助于更​​好地指导光场采集,处理和应用。然而,只有少数客观型号已经提出,并且他们都不完全考虑影响LFI质量的内在因素。在本文中,我们提出了一种无参考光场图像质量评估(NR-LFQA)方案,其中主要思想是通过评估空间质量和角度一致性来量化LFI质量劣化。我们首先通过捕获轻场环形图像阵列的自然分布来测量空间质量劣化,当人类观察LFI时形成的光场环形图像阵列。然后,作为LFI的变换表示,ePipolar平面图像(EPI)包含线的斜率并且涉及角信息。因此,EPI用于从LFI中提取全局和局部特征以测量角度一致性降级。具体地,提出了EPI的梯度方向图的分布,以测量LFI中的全局角度一致性失真。我们进一步提出了加权局部二进制图案,以捕获局部角度一致性劣化的特征。在四个公开可用的LFI质量数据集上进行了广泛的实验结果表明,所提出的方法优于最先进的2D,3D,多视图和LFI质量评估算法。

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