Light field image (LFI) collects radiance from rays in different directions, offers powerful capabilities for immersive media and computer vision. As the high-dimensional data, LFI suffers from spatial as well as angular information distortions in its processing, which brings new challenges to image quality assessment (IQA). Based on the strong ability of tensor about representing high-dimensional data and distortion characteristics of LFI, this paper proposes a method of combining tensor slice and singular value for blind light field image quality assessment (TSSV-LFIQA) to effectively evaluate the quality of LFI content. Specifically, five-order tensor representation of LFI is firstly defined which contains light ray intensity, angular information and color information of the LFI. Secondly, the first slice sharpness measurement and the other slice information distribution are used to describe the tensor slice spatial feature (TSSF) of the LFI. Moreover, singular value angular feature (SVAF) is also proposed to measure the angular consistency of LFI by further unfolding the five-order tensor of LFI and analyzing the percentage of singular values. The experimental results show that benefiting from the combination of TSSF and SVAF, the proposed TSSV-LFIQA method is statistically superior to the existing IQA methods, and matches well with human subjective opinions.

中文翻译：

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结合张量切片和奇异值进行盲光场图像质量评估

光场图像（LFI）从不同方向的光线中收集辐射，为沉浸式媒体和计算机视觉提供强大的功能。作为高维数据，LFI在处理过程中会遭受空间以及角度信息失真的困扰，这给图像质量评估（IQA）带来了新的挑战。基于张量具有较强的高维数据表示能力和LFI的畸变特性，提出了一种将张量片与奇异值相结合的盲光场图像质量评估方法（TSSV-LFIQA），以有效地评估LFI的质量。内容。具体地，首先定义LFI的五阶张量表示，其包含LFI的光线强度，角度信息和颜色信息。第二，第一个切片清晰度测量和其他切片信息分布用于描述LFI的张量切片空间特征（TSSF）。此外，还提出了奇异值角特征（SVAF），通过进一步展开LFI的五阶张量并分析奇异值的百分比来测量LFI的角一致性。实验结果表明，受益于TSSF和SVAF的结合，提出的TSSV-LFIQA方法在统计学上优于现有的IQA方法，并且与人的主观意见相吻合。还提出了奇异值角特征（SVAF），通过进一步展开LFI的五阶张量并分析奇异值的百分比来测量LFI的角一致性。实验结果表明，受益于TSSF和SVAF的结合，提出的TSSV-LFIQA方法在统计学上优于现有的IQA方法，并且与人的主观意见相吻合。还提出了奇异值角特征（SVAF），通过进一步展开LFI的五阶张量并分析奇异值的百分比来测量LFI的角一致性。实验结果表明，受益于TSSF和SVAF的结合，提出的TSSV-LFIQA方法在统计学上优于现有的IQA方法，并且与人的主观意见相吻合。