Deep learning model for real-time image compression in Internet of Underwater Things (IoUT)
Deep learning model for real-time image compression in Internet of Underwater Things (IoUT)
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DOI：
10.1007/s11554-019-00879-6
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发表时间：
2020-12-01
影响因子：
3
通讯作者：
Shankar, K.
中科院分区：
文献类型：
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作者：
Krishnaraj, N.;Elhoseny, Mohamed;Shankar, K.
近年来，物联网（IoT）技术的发展使其在水下环境中的应用得到了扩展，从而催生了水下物联网（IoUT）这一新的应用领域。它提供了更广泛的应用，如大气观测，海洋动物栖息地监测，国防和灾害预测。由于水下环境的特殊性，智能水下物体捕获图像的数据传输非常具有挑战性，需要一种有效的图像传输策略。在本文中，我们建模并实现了一个基于离散小波变换（DWT）的深度学习模型，用于IOUT中的图像压缩。为了实现更好的重建图像质量的有效压缩，卷积神经网络（CNN）被用于编码以及解码侧。我们使用大量的实验来验证DWT-CNN模型，并描述了所提出的深度学习模型在压缩性能和重建图像质量方面上级现有的方法，如超分辨率卷积神经网络（SRCNN），JPEG和JPEG 2000。DWT-CNN模型的平均峰值信噪比（PSNR）为53.961，平均空间节省（SS）为79.7038%。
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%.