In the design of three-dimensional image processing systems, stereoscopic image quality assessment (SIQA) plays an indispensable role as a performance evaluator and a supervisor, yet the study upon which remains immature due to the complexity of the human visual system (HVS). In this study, a novel SIQA method is proposed by extracting quality-aware image features according to the properties of the hierarchical structure in the HVS. Especially, the interests of the primary and secondary visual cortex are taken into consideration, so that the image quality representation is constructed in a way both accurate and efficient. Moreover, influences caused by binocular effects including binocular rivalry and binocular visual discomfort are accounted to further improve the performance of the proposed method. The superiority of the proposed method is validated through experiments on public databases in comparison to state-of-the-art works in terms of accuracy and robustness.