A Novel Lightweight and Robust Source-Channel Coding Solution for MIoT Communication Based on DL Denoising/Super Resolution Model
Résumé
Multimedia Internet of Things (MIoT) network is prone to a variety of
challenging constraints, especially in terms of performance and
security. Employed MIoT devices can be limited in terms of power,
computation, and memory, which make them suffer with the high volume of
collected multimedia data. In this context, data compression is one
solution to reduce the size of communicated data. However, existing
lossy multimedia compression algorithms, such as JPEG and BPG, impose a
practical challenge for several MIoT devices since they require high
computation and memory resources. Another challenge is the errors that
can occur during transmission due to channel errors, which require
re-transmitting the erroneous data. In this case, channel coding is one
solution. However, channel coding solutions impose overhead in terms of
computation and communication resources. To reduce this overhead, in
this paper, we propose a lightweight source and channel coding solution
that should be applied only on the application server. This solution
consists of down-scaling each input image with a factor α ≥ 2 at the
MIoT device. This reduces the computation, communicated data size, and
memory consumption, which would consequently reduce both energy
consumption and latency. However, the down-scaled image might be
corrupted by channel errors due to the reliance on the wireless
connection. In addition, if the down-scaled image was encrypted, an
error in one data block will propagate to other data blocks after the
decryption process at the application server. Thus, to avoid costly data
re-transmission or redundancy, our solution proposes to apply a Deep
Learning (DL) denoising/super-resolution model at the server-side to
recuperate high-quality images. This model plays the role of source and
channel coding algorithm. The obtained results show the effectiveness of
the proposed solution, especially in terms of enhancing the visual
quality of the reconstructed high-resolution images from downscal[...]