  Next-generation ground-based solar observations require good image quality
metrics for post-facto processing techniques. Based on the assumption that
texture features in solar images are multi-fractal which can be extracted by a
trained deep neural network as feature maps, a new reduced-reference objective
image quality metric, the perception evaluation is proposed. The perception
evaluation is defined as cosine distance of Gram matrix between feature maps
extracted from high resolution reference image and that from blurred images. We
evaluate performance of the perception evaluation with simulated and real
observation images. The results show that with a high resolution image as
reference, the perception evaluation can give robust estimate of image quality
for solar images of different scenes.
