• Chitra Wasnik
• Prathamesh Tamanekar
• Nishant Joshi
• Shubham Deokar

Over the past decade, due to the exponential rise in the number of internet and computer users, a colossal amount of data is transferred and used on a daily basis. To enable such a magnitude of data transfer quickly and seamlessly, data compression has become a very crucial part of the mechanism. Thus, we aim to aid and improve the pre-existing image compression algorithms using Artificial Intelligence and Deep learning. The focus of this research and development efforts is on increasing the algorithm speed and compression ratio while achieving a near-lossless compression. We strive to program a neural network for compressing an image while allowing a threshold data loss and later enhancing the image to make up for the lost data, this approach will allow us to reduce the time required for compressing a file as lossy compression is carried out. At the same time, the image enhancement engine manages the data loss and keeps it to a minimum. The applications of such algorithms are infinite, ranging from image transfer, video streaming, and video calls to enhance the cloud experience.

"Image Compression using Generative Adversarial Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.b533-b540, August-2022, Available :http://www.jetir.org/papers/JETIR2208164.pdf

"Image Compression using Generative Adversarial Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. ppb533-b540, August-2022, Available at : http://www.jetir.org/papers/JETIR2208164.pdf