  Given the voluminous nature of the multimedia sensed data, the Multimedia
Internet of Things (MIoT) devices and networks will present several limitations
in terms of power and communication overhead. One traditional solution to cope
with the large-size data challenge is to use lossy compression. However,
current lossy compression schemes require low compression rate to guarantee
acceptable perceived image quality, which results in a low reduction of the
communicated data size and consequently a low reduction in the energy and
bandwidth consumption. Thus, an efficient compression solution is required for
striking a good balance between data size (and consequently communication
overhead) and visual degradation. In this paper, a Deep-Learning (DL)
super-resolution model is applied to recuperate high quality images (at the
application server side) given as input degraded images with a high compression
ratio (at the sender side). The experimental analysis shows the effectiveness
of the proposed solution in enhancing the visual quality of the compressed and
down-scaled images. Consequently, the proposed solution reduces the overall
communication overhead and power consumption of limited MIoT devices.
