A Deep Learning Scheme for Efficient Multimedia IoT Data Compression

TL;DR

This paper proposes a deep learning-based super-resolution method to improve image quality in multimedia IoT data compression, effectively reducing communication overhead and power consumption.

Contribution

It introduces a novel DL super-resolution scheme that balances high compression ratios with acceptable image quality for MIoT devices.

Findings

Enhanced visual quality of compressed images

Reduced communication overhead in MIoT networks

Lower power consumption of IoT devices

Abstract

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.