  The use of 3D and stereo imaging is rapidly increasing. Compression,
transmission, and processing could degrade the quality of stereo images.
Quality assessment of such images is different than their 2D counterparts.
Metrics that represent 3D perception by human visual system (HVS) are expected
to assess stereoscopic quality more accurately. In this paper, inspired by
brain sensory/motor fusion process, two stereo images are fused together. Then
from every fused image two synthesized images are extracted. Effects of
different distortions on statistical distributions of the synthesized images
are shown. Based on the observed statistical changes, features are extracted
from these synthesized images. These features can reveal type and severity of
distortions. Then, a stacked neural network model is proposed, which learns the
extracted features and accurately evaluates the quality of stereo images. This
model is tested on 3D images of popular databases. Experimental results show
the superiority of this method over state of the art stereo image quality
assessment approaches
