Version 1
: Received: 2 August 2023 / Approved: 3 August 2023 / Online: 4 August 2023 (07:21:52 CEST)
How to cite:
Garg, G.; Kumar, R. MRI Images Compression by Deep Convolution Network with Optimize Pixel Predictor. Preprints2023, 2023080364. https://doi.org/10.20944/preprints202308.0364.v1
Garg, G.; Kumar, R. MRI Images Compression by Deep Convolution Network with Optimize Pixel Predictor. Preprints 2023, 2023080364. https://doi.org/10.20944/preprints202308.0364.v1
Garg, G.; Kumar, R. MRI Images Compression by Deep Convolution Network with Optimize Pixel Predictor. Preprints2023, 2023080364. https://doi.org/10.20944/preprints202308.0364.v1
APA Style
Garg, G., & Kumar, R. (2023). MRI Images Compression by Deep Convolution Network with Optimize Pixel Predictor. Preprints. https://doi.org/10.20944/preprints202308.0364.v1
Chicago/Turabian Style
Garg, G. and Raman Kumar. 2023 "MRI Images Compression by Deep Convolution Network with Optimize Pixel Predictor" Preprints. https://doi.org/10.20944/preprints202308.0364.v1
Abstract
The primary goal of picture compression is to reduce the amount of unused image data while still storing or transmitting it in a format that is appropriate. The compression of raw binary data is quite different from the compression of a picture, and these differences may be rather substantial. In light of this, compression is often regarded as an essential technique for the purposes of both data storage an d transmission in order to mitigate the excessive amounts of data that are generated by these images. In order to transmit enormous datasets, particularly for the purposes of telemedicine and teleradiology, one needs a significant amount of storage capacity as well as an expansive network. As a result, compression is an important aspect of medical imaging. In addition to the importance of compression, the quality of the photos themselves is also an essential factor in the success of analysis. In addition to this, the amount of time necessary to compress the photographs before sending them should be reduced.
Keywords
Medical images; Compression; CNN; LMCDP; Prediction
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
