Light field image (LFI) records the radiation intensity and direction information of light in scene, offering powerful capabilities for computer vision. It is found that the distortion caused by LFI processing is obviously reflected in the two representations of LFI, that is, subaperture images (SAIs) and microlens images (MLI). Specifically, on the one hand, pseudoreference SAI (PSAI) of a distorted SAI, which is rendered with the adjacent distorted SAIs, can be used to measure the distortion of the involved SAIs. On the other hand, macropixel blocks (MPBs) of MLI can be adopted to measure LFI global angular distortion. Therefore, a PSAIs- and MLI-based blind LFI quality measurement (PM-BLFIQM) method is proposed in this article. First, the local light field (LLF) is defined to generate PSAI of the central distorted SAI in each LLF of LFI, and then a pseudo-full-reference quality measurement metric based on PSAIs is presented. Second, to measure the angular consistency of the distorted LFI, an MLI-based feature extraction scheme is proposed for MPBs and their mean removal version by using discrete cosine transform and singular value decomposition. Finally, the quality of the distorted LFI is predicted with the features extracted from LLFs and MLI. Experimental results demonstrate that both PSAI and MPB are effective for measuring the quality of distorted LFI. In addition, the results on three LFI databases show that the proposed method achieves higher Spearman rank-order correlation coefficient (SROCC) and Pearson linear correlation coefficient (PLCC) values, so it outperforms the state-of-the-art methods in terms of consistency with human visual perception.

中文翻译：

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伪参考子孔径图像和基于微透镜图像的盲光场图像质量测量


光场图像（LFI）记录了场景中光的辐射强度和方向信息，为计算机视觉提供了强大的能力。发现LFI处理引起的畸变明显地体现在LFI的两种表示形式上，即子孔径图像（SAI）和微透镜图像（MLI）。具体地，一方面，与相邻的扭曲SAI一起渲染的扭曲SAI的伪参考SAI（PSAI）可用于测量所涉及的SAI的扭曲。另一方面，可以采用MLI的宏像素块（MPB）来测量LFI全局角度畸变。因此，本文提出了一种基于PSAIs和MLI的盲LFI质量测量（PM-BLFIQM）方法。首先，定义局部光场（LLF）以生成LFI的每个LLF中的中心畸变SAI的PSAI，然后提出基于PSAI的伪全参考质量测量度量。其次，为了测量扭曲 LFI 的角度一致性，通过使用离散余弦变换和奇异值分解，提出了一种基于 MLI 的 MPB 特征提取方案及其均值去除版本。最后，利用从 LLF 和 MLI 中提取的特征来预测失真 LFI 的质量。实验结果表明PSAI和MPB对于测量失真LFI的质量都是有效的。此外，三个 LFI 数据库的结果表明，所提出的方法实现了更高的 Spearman 排序相关系数（SROCC）和 Pearson 线性相关系数（PLCC）值，因此在以下方面优于最先进的方法：与人类视觉感知的一致性。