Pyramid Feature Aggregation for Hierarchical Quality Prediction of Stitched Panoramic Images

Panoramic image quality assessment (PIQA) is crucial to the successful application of technologies that can provide immersive visual experience. Stitching distortions are one of the main types of distortions that result in panoramic image degradation. However, most existing PIQA methods are general-purpose ones, which ignore the special characteristics of the stitching distortions caused by imperfect stitching algorithms. This results in unsatisfactory performance. To this end, we propose an effective stitched PIQA method, which consists of an imaginary reference generation (IRG) module and a hierarchical quality prediction (HQP) module. Among them, the IRG module is proposed to mimic the capability of the human visual system in imagining the raw version in the face of a degraded image. For the IRG module learning, we construct a large-scale database. The HQP module is presented to adapt to the particularity and complexity of stitching distortions, which is achieved by the pyramid feature aggregation. Extensive experiments and comparisons have been performed on the stitched PIQA database and the experimental results demonstrate the superiority of the proposed method in evaluating the quality of stitched panoramic images.

Automated summary

Panoramic image quality assessment (PIQA) is crucial for technologies providing immersive visual experience. Most existing PIQA methods ignore the special characteristics of stitching distortions caused by imperfect algorithms, resulting in unsatisfactory performance. To address this, we propose an effective stitched PIQA method consisting of an imaginary reference generation module and a hierarchical quality prediction module. Extensive experiments demonstrate the superiority of our method in evaluating the quality of stitched panoramic images.