Representation Learning in Wireless Multimedia Communications

Xiaoming Tao, Yiping Duan, Cheng Yang, Haijun Zhang, Shuai Liu, Jianhua Lu

Abstract: With the advent of 5G wireless communication systems, multimedia data is predicted to grow rapidly in the near future. As a result, the large amount of multimedia data puts huge pressure on wireless communication, which poses a huge challenge to network capacity. Multimedia data (image/ video) intelligent compression is an effective way to increase the network capacity and improve the user's QoE. The success of image/video compression generally depends on data representations. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation learning algorithms implementing such priors. This article proposes an intelligent computing communication framework to reduce the amount of transferred data. Simultaneously, it reviews the area of representation learning, covering advances in dictionary learning, RoI, and deep learning image/video compression methods. Furthermore, we compare various compression methods as well as point out promising future works.

External IDs:dblp:journals/wc/TaoDYZLL20