360-degree image quality assessment using deep neural networks is usually designed using a multi-channel paradigm exploiting possible viewports. This is mainly due to the high resolution of such images and the unavailability of ground truth labels (subjective quality scores) for individual viewports. The multi-channel model is hence trained to predict the score of the whole 360-degree image. However, this comes with a high complexity cost as multi neural networks run in parallel. In this paper, a patch-based training is proposed instead. To account for the non-uniformity of quality distribution of a scene, a weighted pooling of patches’ scores is applied. The latter relies on natural scene statistics in addition to perceptual properties related to immersive environments.