Abstract
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.
Abstract (translated)
近年来，大规模采用云存储解决方案彻底改变了我们对待数字数据存储的想法。然而，数据量的指数增长，特别是图像，引起了关于能源和资源消耗以及数字碳排放足迹的环保担忧。本研究旨在提出一种将图像压缩技术集成到超分辨率生成对抗网络（SRGAN）中的云图像存储方法。我们不直接将图像存储在云中，而是首先通过压缩和压缩裁剪等方法减小图像尺寸。在请求时，这些压缩图像将由SRGAN检索和处理以生成图像。所提出方法的有效性在PSNR和SSIM指标上进行评估。此外，还给出了计算能耗和碳足迹评估的数学分析。所提出的数据压缩技术为实现工业效率与环保之间的良好平衡提供了一个显著的解决方案。
URL
https://arxiv.org/abs/2404.04642
