Learned Scanpaths Aid Blind Panoramic Video Quality Assessment
Kanglong Fan Wen Wen Mu Li Yifan Peng Kede Ma
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.