According to Cisco, most Internet traffic is currently comprised of videos. Therefore, developing a quality assessment method for assuring that those videos are received and displayed with quality at the user side is an important and challenging task. As a consequence, over the last decades, several no-reference video quality metrics have been proposed with the goal of blindly predicting (with no access to the original signal) the quality of videos in streaming applications. One of such metrics is NAVE, whose architecture includes an auto-encoder module that produces a compact set of visual features with a higher descriptive capacity. Nevertheless, the visual features in NAVE do not include descriptive temporal features that are sensitive to temporal degradation. In this work, we analyze the effect on accuracy performance of using a new type of temporal features, based on natural scene statistics. This approach has the goal of making the tested video quality metric more generic, i.e. sensitive to both spatial and temporal distortions and therefore adequate for video streaming applications.
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.