This letter presents an automatic and opinion-unaware quality assessment (QA) model for stereoscopic (3-D) images affected by sensor, transmission, and compression distortions. The proposed QA model does not use reference 3-D images or subjective ratings from the primary dataset to map to a quality score. This design is important in 3-D image quality assessment (IQA), where generalization across various 3-D IQA datasets has remained a major challenge. Therefore, to address this gap, we propose a QA model for 3-D images by computing a correlation map between the luminance and depth components. Subsequently, we employ a univariate generalized Gaussian distribution (UGGD) to model the multiscale and multiorientation steerable decomposed subbands and capture the quality-aware representations of the computed correlation map. Then, the spread and the shape parameters of UGGD of the test 3-D image are estimated, and the individual likelihood estimates are calculated from the pristine multivariate Gaussian parameters of the corresponding shape and spread features. Experiments on LIVE Phase I and Phase II datasets show that the proposed opinion-unaware model outperforms existing opinion-unaware models on Phase I and achieves competitive performance against opinion-aware models. On Phase II dataset, it delivers competitive results compared to opinion-aware and opinion-unaware approaches.