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.Comment: Accepted for IEEE VR 2023 (TVCG Special Issues) (Early Access