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Visual comfort is a quite important factor in 3D media service. Few researchefforts have been carried out in this area especially in case of 3D contentretargeting which may introduce more complicated visual distortions. In thispaper, we propose a Hybrid Distortion Aggregated Visual Comfort Assessment(HDA-VCA) scheme for stereoscopic retargeted images (SRI), consideringaggregation of hybrid distortions including structure distortion, informationloss, binocular incongruity and semantic distortion. Specifically, a Local-SSIMfeature is proposed to reflect the local structural distortion of SRI, andinformation 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 thecorrelation distance of paired feature maps extracted from originalstereoscopic image and its retargeted image by using trained deep neuralnetwork. We validate the effectiveness of HDA-VCA on published StereoscopicImage Retargeting Database (SIRD) and two stereoscopic image databases IEEE-SAand NBU 3D-VCA. The results demonstrate HDA-VCA's superior performance inhandling hybrid distortions compared to state-of-the-art VCA schemes.

Hybrid Distortion Aggregated Visual Comfort Assessment for Stereoscopic Image Retargeting