Learned Scanpaths Aid Blind Panoramic Video Quality Assessment
This work addresses the problem of quality assessment for panoramic videos, which is crucial for immersive media applications, but it is incremental as it builds on existing PVQA methods by incorporating scanpath modeling.
The paper tackled the challenge of assessing panoramic video quality by proposing a blind PVQA method that models user viewing patterns through learned scanpaths, achieving superior performance over existing methods on three public datasets with synthetic and authentic distortions.
Panoramic videos have the advantage of providing an immersive and interactive viewing experience. Nevertheless, their spherical nature gives rise to various and uncertain user viewing behaviors, which poses significant challenges for panoramic video quality assessment (PVQA). In this work, we propose an end-to-end optimized, blind PVQA method with explicit modeling of user viewing patterns through visual scanpaths. Our method consists of two modules: a scanpath generator and a quality assessor. The scanpath generator is initially trained to predict future scanpaths by minimizing their expected code length and then jointly optimized with the quality assessor for quality prediction. Our blind PVQA method enables direct quality assessment of panoramic images by treating them as videos composed of identical frames. Experiments on three public panoramic image and video quality datasets, encompassing both synthetic and authentic distortions, validate the superiority of our blind PVQA model over existing methods.