Light field imaging can capture both the intensity information and the direction information of light rays. It naturally enables a six-degrees-of-freedom viewing experience and deep user engagement in virtual reality. Compared to 2D image assessment, light field image quality assessment (LFIQA) needs to consider not only the image quality in the spatial domain but also the quality consistency in the angular domain. However, there is a lack of metrics to effectively reflect the angular consistency and thus the angular quality of a light field image (LFI). Furthermore, the existing LFIQA metrics suffer from high computational costs due to the excessive data volume of LFIs. In this paper, we propose a novel concept of "anglewise attention" by introducing a multihead self-attention mechanism to the angular domain of an LFI. This mechanism better reflects the LFI quality. In particular, we propose three new attention kernels, including anglewise self-attention, anglewise grid attention, and anglewise central attention. These attention kernels can realize angular self-attention, extract multiangled features globally or selectively, and reduce the computational cost of feature extraction. By effectively incorporating the proposed kernels, we further propose our light field attentional convolutional neural network (LFACon) as an LFIQA metric. Our experimental results show that the proposed LFACon metric significantly outperforms the state-of-the-art LFIQA metrics. For the majority of distortion types, LFACon attains the best performance with lower complexity and less computational time.

翻译：光场成像能够同时捕捉光线的强度信息和方向信息。它自然实现了虚拟现实中的六自由度观看体验和深度用户参与。相较于二维图像评估，光场图像质量评估不仅需要考虑空间域的图像质量，还需考虑角度域的质量一致性。然而，当前缺乏能有效反映光场图像角度一致性及其角度质量的度量指标。此外，由于光场图像数据量庞大，现有评估指标计算成本高昂。本文提出"角度注意力"这一新颖概念，通过在光场图像的角度域引入多头自注意力机制来更好地反映光场图像质量。具体而言，我们提出三种新型注意力核，包括角度自注意力、角度网格注意力和角度中心注意力。这些注意力核能够实现角度自注意力、全局或选择性地提取多角度特征，并降低特征提取的计算成本。通过有效整合所提出的注意力核，我们进一步提出光场注意力卷积神经网络作为光场图像质量评估指标。实验结果表明，所提出的光场注意力卷积神经网络指标显著优于当前最先进的光场图像质量评估指标。针对大多数失真类型，该指标以更低的复杂度和更少的计算时间获得了最优性能。