Edge-Assisted Super Resolution for Volumetric Video Enhancement

Volumetric video allows users to watch videos freely from six degrees of freedom, providing a more immersive experience, so it is highly popular among the public. However, volumetric video data is volumes, and it is extremely challenging to achieve reliable transmission and provide users with a good viewing experience. In this paper, we design a volumetric video transmission model based on edge-end collaboration for the characteristics of volumetric video. Through this model, we can achieve the transmission of volumetric video from the server-side to the client-side. Simultaneously, in order to ensure the user's viewing experience, we propose a super-resolution algorithm SRSC based on sparse convolution to enhance video quality. In addition, considering the limited computing resources of the client, we combine mobile edge computing (MEC) to deploy the super-resolution model on the edge. Finally, we conduct an experimental evaluation, and the results show that compared with the existing methods, our proposed algorithm has a significant improvement in performance.