Blind Omnidirectional Image Quality Assessment: Integrating Local Statistics and Global Semantics

Abstract

Omnidirectional image quality assessment (OIQA) aims to predict the perceptual quality of omnidirectional images that cover the whole 180360^ viewing range of the visual environment. Here we propose a blind/no-reference OIQA method named S^2 that bridges the gap between low-level statistics and high-level semantics of omnidirectional images. Specifically, statistic and semantic features are extracted in separate paths from multiple local viewports and the hallucinated global omnidirectional image, respectively. A quality regression along with a weighting process is then followed that maps the extracted quality-aware features to a perceptual quality prediction. Experimental results demonstrate that the proposed S^2 method offers highly competitive performance against state-of-the-art methods.

Tasks

Reproductions