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

Krishnaraj, N. ; Elhoseny, Mohamed ; Thenmozhi, M. ; Selim, Mahmoud M. ; Shankar, K.

Journal of real-time image processing, 2020-12, Vol.17 (6), p.2097-2111 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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Título:
Deep learning model for real-time image compression in Internet of Underwater Things (IoUT)
Autor: Krishnaraj, N. ; Elhoseny, Mohamed ; Thenmozhi, M. ; Selim, Mahmoud M. ; Shankar, K.
Assuntos: 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
É parte de: Journal of real-time image processing, 2020-12, Vol.17 (6), p.2097-2111
Descrição: 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%.
Editor: Berlin/Heidelberg: Springer Berlin Heidelberg
Idioma: Inglês

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

Krishnaraj, N. ; Elhoseny, Mohamed ; Thenmozhi, M. ; Selim, Mahmoud M. ; Shankar, K.

Berlin/Heidelberg: Springer Berlin Heidelberg

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