  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.
