  In this paper, we propose a novel virtual reality image quality assessment
(VR IQA) with adversarial learning for omnidirectional images. To take into
account the characteristics of the omnidirectional image, we devise deep
networks including novel quality score predictor and human perception guider.
The proposed quality score predictor automatically predicts the quality score
of distorted image using the latent spatial and position feature. The proposed
human perception guider criticizes the predicted quality score of the predictor
with the human perceptual score using adversarial learning. For evaluation, we
conducted extensive subjective experiments with omnidirectional image dataset.
Experimental results show that the proposed VR IQA metric outperforms the 2-D
IQA and the state-of-the-arts VR IQA.
