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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 expectedto assess stereoscopic quality more accurately. In this paper, inspired bybrain sensory/motor fusion process, two stereo images are fused together. Thenfrom every fused image two synthesized images are extracted. Effects ofdifferent distortions on statistical distributions of the synthesized imagesare shown. Based on the observed statistical changes, features are extractedfrom these synthesized images. These features can reveal type and severity ofdistortions. Then, a stacked neural network model is proposed, which learns theextracted features and accurately evaluates the quality of stereo images. Thismodel is tested on 3D images of popular databases. Experimental results showthe superiority of this method over state of the art stereo image qualityassessment approaches

Blind Stereo Image Quality Assessment Inspired by Brain Sensory-Motor Fusion