  Visual comfort is a quite important factor in 3D media service. Few research
efforts have been carried out in this area especially in case of 3D content
retargeting which may introduce more complicated visual distortions. In this
paper, we propose a Hybrid Distortion Aggregated Visual Comfort Assessment
(HDA-VCA) scheme for stereoscopic retargeted images (SRI), considering
aggregation of hybrid distortions including structure distortion, information
loss, binocular incongruity and semantic distortion. Specifically, a Local-SSIM
feature is proposed to reflect the local structural distortion of SRI, and
information loss is represented by Dual Natural Scene Statistics (D-NSS)
feature extracted from the binocular summation and difference channels.
Regarding binocular incongruity, visual comfort zone, window violation,
binocular rivalry, and accommodation-vergence conflict of human visual system
(HVS) are evaluated. Finally, the semantic distortion is represented by the
correlation distance of paired feature maps extracted from original
stereoscopic image and its retargeted image by using trained deep neural
network. We validate the effectiveness of HDA-VCA on published Stereoscopic
Image Retargeting Database (SIRD) and two stereoscopic image databases IEEE-SA
and NBU 3D-VCA. The results demonstrate HDA-VCA's superior performance in
handling hybrid distortions compared to state-of-the-art VCA schemes.
