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Given the voluminous nature of the multimedia sensed data, the MultimediaInternet of Things (MIoT) devices and networks will present several limitationsin terms of power and communication overhead. One traditional solution to copewith the large-size data challenge is to use lossy compression. However,current lossy compression schemes require low compression rate to guaranteeacceptable perceived image quality, which results in a low reduction of thecommunicated data size and consequently a low reduction in the energy andbandwidth consumption. Thus, an efficient compression solution is required forstriking a good balance between data size (and consequently communicationoverhead) and visual degradation. In this paper, a Deep-Learning (DL)super-resolution model is applied to recuperate high quality images (at theapplication server side) given as input degraded images with a high compressionratio (at the sender side). The experimental analysis shows the effectivenessof the proposed solution in enhancing the visual quality of the compressed anddown-scaled images. Consequently, the proposed solution reduces the overallcommunication overhead and power consumption of limited MIoT devices.

A Deep Learning Scheme for Efficient Multimedia IoT Data Compression