Blind quality assessment for multiply distorted stereoscopic images towards IoT-based 3D capture systems

Empowered by 5G mobile communication networks, multimedia processing has been considered as a very promising application of Internet-of-Things (IoT). Stereoscopic image quality assessment (SIQA), as an important part of 3D capture system, can be embedded in the cloud or fog servers to automatically monitor the perceptual quality of the collected stereoscopic images. In this paper, a novel blind image quality assessment method towards IoT-based 3D capture systems is developed for multiply-distorted stereoscopic images (MDSIs), in which five complementary channels, including left view, right view, cyclopean map, summation map and difference map, are jointly considered in dictionary learning for characterizing the monocular receptive field (MRF) and binocular receptive field (BRF) properties of the visual cortex in response to MDSIs. Additionally, the high order statistics scheme is adopted by utilizing the statistical differences between the codebook and images to ensure the stable and robust quality prediction performance for MDSIs. The proposed method shows competitive prediction performances on four benchmark databases compared with the existing SIQA metrics.