  Stereoscopic image quality measurement (SIQM) has become increasingly
important for guiding stereo image processing and commutation systems due to
the widespread usage of 3D contents. Compared with conventional methods which
are relied on hand-crafted features, deep learning oriented measurements have
achieved remarkable performance in recent years. However, most existing deep
SIQM evaluators are not specifically built for stereoscopic contents and
consider little prior domain knowledge of the 3D human visual system (HVS) in
network design. In this paper, we develop a Predictive Auto-encoDing Network
(PAD-Net) for blind/No-Reference stereoscopic image quality measurement. In the
first stage, inspired by the predictive coding theory that the cognition system
tries to match bottom-up visual signal with top-down predictions, we adopt the
encoder-decoder architecture to reconstruct the distorted inputs. Besides,
motivated by the binocular rivalry phenomenon, we leverage the likelihood and
prior maps generated from the predictive coding process in the Siamese
framework for assisting SIQM. In the second stage, quality regression network
is applied to the fusion image for acquiring the perceptual quality prediction.
The performance of PAD-Net has been extensively evaluated on three benchmark
databases and the superiority has been well validated on both symmetrically and
asymmetrically distorted stereoscopic images under various distortion types.
