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

## TL;DR

This paper introduces S$^2$, a blind omnidirectional image quality assessment method that combines local statistical features and global semantic features to accurately predict perceptual quality.

## Contribution

The paper presents a novel approach that integrates local statistics and global semantics for blind omnidirectional image quality assessment, improving prediction accuracy.

## Key findings

- S$^2$ achieves competitive performance against state-of-the-art methods.
- Combining local statistics and global semantics enhances quality prediction.
- The method effectively predicts perceptual quality without reference images.

## Abstract

Omnidirectional image quality assessment (OIQA) aims to predict the perceptual quality of omnidirectional images that cover the whole 180$\times$360$^{\circ}$ 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.