No-Reference Stereoscopic Omnidirectional Image Quality Assessment via a Binocular Viewport Hypergraph Convolutional Network

Omnidirectional images, offering immersive 360° views, have gained significant attention, but assessing their perceptual quality, especially for stereoscopic content, remains a complex challenge. A major limitation lies in the fact that head-mounted devices restrict the viewer’s experience to a single viewport at a time, necessitating a comprehensive understanding of how multiple viewport images interact and aggregate during the viewing process. Moreover, the depth dimension inherent in stereoscopic content further complicates the 360° visual experience, a factor often oversimplified by existing methods, limiting their ability to accurately differentiate perceptual quality across viewports. To address these challenges, we propose a novel no-reference quality assessment model for stereoscopic omnidirectional images. Our approach integrates binocular vision principles within a viewport hypergraph convolutional network framework. First, guided by the unique viewing patterns of stereoscopic omnidirectional images, our model selects panoramic viewports that align with human visual preferences. Next, we devise an image feature extraction network that simulates the binocular fusion and rivalry mechanisms within the human visual system, leveraging a twin encoder-decoder network and tensor decomposition to capture key features. Finally, to assess overall image quality, we introduce a hypergraph structure module that captures complex positional and content-based interactions among sampled viewports through the Graph Influence Network. Extensive experiments on the NBU-SOID, SOLID, and LIVE 3D VR databases demonstrate the superior accuracy and robustness of our model compared to state-of-the-art methods.