Image compression became prevalent for every image and graphic that we want to send over the internet. It is because a significant amount of bandwidth and space decrease using the image compression techniques. It reduces both time and bandwidth cost and leads to fast and efficient sharing of images and other information. Neural networks have been researching rapidly for image processing. In this research, we presented image compression and error correction control using deep neural networks. First, Deep Neural Network (DNN) is implemented for image compression. We applied the algorithm on four different sizes of images including 512*512, 256*256, 128*128 and 64*64. The result of DNN implementation shows better quality of the decompressed images along with less computational capacity. The proposed algorithm is effective in accomplishing better errorcorrection and reducing the storage requirements.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.