The rapid growth of stereoscopic (S3D) imaging has led to increased adoption across multimedia formats. However, the S3D pipeline introduces spatial, depth, and binocular rivalry errors, degrading visual quality. Quality assessment (QA) helps evaluate and improve the visual experience, ensuring comfort during prolonged exposure to S3D content. This article proposes an “opinion-unaware”QA model for S3D images based on statistical variations in the correlation map between color and depth features. The quality score of the correlation map is obtained using principal component analysis (PCA) and multivariate Gaussian modeling. A natural image quality evaluator calculates spatial scores from the luminance information of an S3D image. Finally, the overall S3D image quality is determined by a weighted mechanism that combines color, depth, and spatial scores. The weighted pooling includes the spatial score, which is otherwise ignored in correlation map calculation. Extensive experiments on four standard datasets, namely, LIVE Phase I and Phase II, Waterloo Phase I and Phase II, have demonstrated that the proposed model outperforms off-the-shelf opinion-unaware models and gives competitive performance to supervised no-reference (NR) models.