Power-Efficient Image Storage: Leveraging Super Resolution Generative
  Adversarial Network for Sustainable Compression and Reduced Carbon Footprint
Ashok Mondal Satyam Singh
In recent years, large-scale adoption of cloud storage solutions has
revolutionized the way we think about digital data storage. However, the
exponential increase in data volume, especially images, has raised
environmental concerns regarding power and resource consumption, as well as the
rising digital carbon footprint emissions. The aim of this research is to
propose a methodology for cloud-based image storage by integrating image
compression technology with SuperResolution Generative Adversarial Networks
(SRGAN). Rather than storing images in their original format directly on the
cloud, our approach involves initially reducing the image size through
compression and downsizing techniques before storage. Upon request, these
compressed images will be retrieved and processed by SRGAN to generate images.
The efficacy of the proposed method is evaluated in terms of PSNR and SSIM
metrics. Additionally, a mathematical analysis is given to calculate power
consumption and carbon footprint assesment. The proposed data compression
technique provides a significant solution to achieve a reasonable trade off
between environmental sustainability and industrial efficiency.