Effect of Channel Consideration on Auto Encoders for Color Image Compression using Deep Learning 
G. Ruth Rajitha Rani1, Ch. Samson2
1G. Ruth Rajitha Rani*, Department of Information Technology, Osmania University, Hyderabad (Telangana), India.
2Ch. Samson, Professor, Department of Information Technology, MVSR Engineering College, Hyderabad (Telangana), India.
Manuscript received on December 15, 2021. | Revised Manuscript received on December 20, 2021. | Manuscript published on December 30, 2021.| PP: 72-74 | Volume-11 Issue-2, December 2021. | Retrieval Number: 100.1/ijeat.B33101211221 | DOI: 10.35940/ijeat.B3310.1211221
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, we have studied the effect of channels consideration on autoencoders for color image compression. The study is made in relation to RGB patch in an image and individual channel patches to know the effectiveness of what criteria is to be used while processing the image for compression. The study reveals that the RGB patch consideration in a color image is better than considering the channels individually. The chaotic (or scramble) image is given as input to autoencoder for compression and this helps to overcome the threat by the intruder and as well protection to data transmitted.
Keywords: Autoencoder-Decoder, Chaotic, Image Compression.
Scope of the Article: Deep Learning.