A Blind Stereoscopic Image Quality Evaluator with Segmented Stacked Autoencoders Considering The Whole Visual Perception Route.

Most of the current blind stereoscopic image quality assessment (SIQA) algorithms cannot show reliable accuracy. One reason is that they do not have the deep architectures and the other reason is that they are designed on the relatively weak biological basis, compared with findings on human visual system (HVS). In this paper, we propose a Deep Edge and COlor Signal INtegrity Evaluator (DECOSINE) based on the whole visual perception route from eyes to the frontal lobe, and especially focus on edge and color signal processing in retinal ganglion cells (RGC) and lateral geniculate nucleus (LGN).

No-reference stereoscopic image quality assessment based on hue summation-difference mapping image and binocular joint mutual filtering.

The no-reference (NR) quality assessment for stereoscopic images plays a significant role in 3D technology, but it also faces great challenges. In this paper, a novel NR stereo image quality assessment (SIQA) method is proposed. Based on the human visual system, this method mimics the summation and difference channels, which consider the binocular interactive perception property, to process the visual information.