Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views
This addresses a gap in immersive imaging systems for researchers and developers by providing a novel assessment tool, though it is incremental as it adapts existing metrics to a new domain.
The paper tackles the lack of quality assessment methods for super-resolved omnidirectional images by proposing a full-reference framework that uses tangential views to evaluate GAN-based and CNN-based super-resolution techniques, finding that objective metrics favor CNN-based methods while subjective tests prefer GAN-based ones.
Omnidirectional images (ODIs), also known as 360-degree images, enable viewers to explore all directions of a given 360-degree scene from a fixed point. Designing an immersive imaging system with ODI is challenging as such systems require very large resolution coverage of the entire 360 viewing space to provide an enhanced quality of experience (QoE). Despite remarkable progress on single image super-resolution (SISR) methods with deep-learning techniques, no study for quality assessments of super-resolved ODIs exists to analyze the quality of such SISR techniques. This paper proposes an objective, full-reference quality assessment framework which studies quality measurement for ODIs generated by GAN-based and CNN-based SISR methods. The quality assessment framework offers to utilize tangential views to cope with the spherical nature of a given ODIs. The generated tangential views are distortion-free and can be efficiently scaled to high-resolution spherical data for SISR quality measurement. We extensively evaluate two state-of-the-art SISR methods using widely used full-reference SISR quality metrics adapted to our designed framework. In addition, our study reveals that most objective metric show high performance over CNN based SISR, while subjective tests favors GAN-based architectures.