  Promising results for subjective image quality prediction have been achieved
during the past few years by using convolutional neural networks (CNN).
However, the use of CNNs for high resolution image quality assessment remains a
challenge, since typical CNN architectures have been designed for small
resolution input images. In this study, we propose an image quality model that
attempts to mimic the attention mechanism of human visual system (HVS) by using
a recurrent neural network (RNN) for spatial pooling of the features extracted
from different spatial areas (patches) by a deep CNN-based feature extractor.
The experimental study, conducted by using images with different resolutions
from two recently published image quality datasets, indicates that the quality
prediction accuracy of the proposed method is competitive against benchmark
models representing the state-of-the-art, and the proposed method also performs
consistently on different resolution versions of the same dataset.
