Open AccessEffect of Channel Consideration on Auto Encoders for Color Image Compression using Deep Learning
Author(s) -
G. Ruth Rajitha Rani,
AUTHOR_ID,
Ch. Samson,
AUTHOR_ID
Publication year - 2021
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b3310.1211221
Subject(s) - artificial intelligence , rgb color model , computer science , color image , computer vision , channel (broadcasting) , autoencoder , image compression , image (mathematics) , relation (database) , compression (physics) , image processing , deep learning , telecommunications , data mining , materials science , composite material
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.