Deep learning model for real-time image compression in Internet of Underwater Things (IoUT)

Recently, the advancements of Internet-of-Things (IoT) have expanded its application in underwater environment which leads to the development of a new field of Internet of Underwater Things (IoUT). It offers a broader view of applications such as atmosphere observation, habitat monitoring of sea ani...

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Vydané v:Journal of real-time image processing Ročník 17; číslo 6; s. 2097 - 2111
Hlavní autori: Krishnaraj, N., Elhoseny, Mohamed, Thenmozhi, M., Selim, Mahmoud M., Shankar, K.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020
Springer Nature B.V
Predmet:
Algorithms
Artificial neural networks
Coding standards
Communication
Computer Graphics
Computer Science
Data compression
Data transmission
Deep learning
Discrete Wavelet Transform
Energy efficiency
Image coding
Image compression
Image Processing and Computer Vision
Image quality
Image reconstruction
Image transmission
Internet of Things
Machine learning
Multimedia Information Systems
Neural networks
Pattern Recognition
Propagation
Sensors
Signal to noise ratio
Signal,Image and Speech Processing
Special Issue Paper
Underwater
Deep learning
Image compression
Image reconstruction
IoUT
Underwater
ISSN:1861-8200, 1861-8219
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Popis
Shrnutí:Recently, the advancements of Internet-of-Things (IoT) have expanded its application in underwater environment which leads to the development of a new field of Internet of Underwater Things (IoUT). It offers a broader view of applications such as atmosphere observation, habitat monitoring of sea animals, defense and disaster prediction. Data transmission of images captured by the smart underwater objects is very challenging due to the nature of underwater environment and necessitates an efficient image transmission strategy for IoUT. In this paper, we model and implement a discrete wavelet transform (DWT) based deep learning model for image compression in IoUT. For achieving effective compression with better reconstruction image quality, convolution neural network (CNN) is used at the encoding as well as decoding side. We validate DWT–CNN model using extensive set of experimentations and depict that the presented deep learning model is superior to existing methods such as super-resolution convolutional neural networks (SRCNN), JPEG and JPEG2000 in terms of compression performance as well as reconstructed image quality. The DWT–CNN model attains an average peak signal-to-noise ratio (PSNR) of 53.961 with average space saving (SS) of 79.7038%.
Bibliografia:ObjectType-Article-1
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ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-019-00879-6