Lightweight Super-Resolution with Self-Calibrated Convolution for Panoramic Videos

Abstract

Panoramic videos are shot by an omnidirectional camera or a collection of cameras, and can display a view in every direction. They can provide viewers with an immersive feeling. The study of super-resolution of panoramic videos has attracted much attention, and many methods have been proposed, especially deep learning-based methods. However, due to complex architectures of all the methods, they always result in a large number of hyperparameters. To address this issue, we propose the first lightweight super-resolution method with self-calibrated convolution for panoramic videos. A new deformable convolution module is designed first, with self-calibration convolution, which can learn more accurate offset and enhance feature alignment. Moreover, we present a new residual dense block for feature reconstruction, which can significantly reduce the parameters while maintaining performance. The performance of the proposed method is compared to those of the state-of-the-art methods, and is verified on the MiG panoramic video dataset.

Research on the design of panoramic virtual learning environment screen elements

“…Panoramic video is the product of a combination of multiple video technologies. Such videos allow the audience to have a wider field of view and more realistic field of view experiences ( Shang et al, 2022 ). While many studies have shown that panoramic videos are beneficial for learning, some evidence that panoramic videos are detrimental to learning has also emerged.…”