TFFN: Three-Branch Feature Fusion Network for Stereoscopic Omnidirectional Image Quality Assessment

Stereoscopic omnidirectional image (SOI) has both omnidirectional and stereoscopic perception features. Many previous models have proved the viewport characteristics and stereoscopic visual features are crucial for quality perception of SOI. However, effective monocular and binocular visual features extraction and fusion are difficult due to the size of SOI and inaccuracy of feature representation. In this paper, we proposed a three-branch feature fusion network (TFFN) by fusing two-stream binocular visual features and the important monocular features based on the viewport perspective. The hierarchical fusion module is first designed to fuse effective binocular visual features from different semantic scales, and the pseudo-difference information extraction module is built to obtain the accuracy monocular visual features to complement the binocular visual features. Finally, the above monocular and binocular visual features are fused together to measure the quality of SOI. The comparison experiments are conducted on three public datasets and the analysis of the results demonstrate the effectiveness of the proposed method.