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ICTACT Journal on Image and Video Processing ( Volume: 14 , Issue: 4 )
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Due to the explosion of video based information proliferating in the
world due to the ubiquitous usage of video cameras the amount of video
based information that is currently being generated around the world
is huge. And due to security purposes it is becoming imperative that
these video data needs to be stored in computer memory for an extended
period of time for referrals by security agencies. Because of the
advancement of imaging technologies that is being used nowadays it is
possible to capture extremely detailed high definition images. But it is
not physically possible to store all these high-definition images in
computer memories for a long time because infrastructure providers
will run out of memory. Image compression is a technology which
assists us in this regard. Nowadays this technology has moved from
image compression to video compression to compression of 3-
dimensional videos which is now becoming more and more popular due
to the ever increasing usage of Augmented Reality, Virtual Reality and
Ec The quantity of image data produced in modern surveillance
networks is rising exponentially year on year which necessitated
development of novel schemes for reducing the sizes of the images
captured by CCTV cameras while not compromising on the image
quality increases when the images are decompressed. This paper
proposed a novel Deep Neural Network based method of compressing
images in which the image accuracy is not lost but the space it occupies
in the memory storage of the computes is reduced greatly compared to
other image compression schemes, this proposed scheme is best suited
for usage in CCTVs and other networks using Internet of Things which
record images and videos continuously.
Shivganga Patil, Lakshmi Patil
Sharnbasva University, India
S-BHM, Slimming Encoders, Image Compression
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 14 , Issue: 4 , Pages: 3301 - 3304 )
Date of Publication :
May 2024
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