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Journal of Sensors
Volume 2016 (2016), Article ID 3184840, 7 pages
Research Article

Efficient Deep Neural Network for Digital Image Compression Employing Rectified Linear Neurons

Department of Electronics & Computer Engineering, Hanyang University, Seoul 133-791, Republic of Korea

Received 16 December 2014; Accepted 11 February 2015

Academic Editor: Wei Wu

Copyright © 2016 Farhan Hussain and Jechang Jeong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A compression technique for still digital images is proposed with deep neural networks (DNNs) employing rectified linear units (ReLUs). We tend to exploit the DNNs capabilities to find a reasonable estimate of the underlying compression/decompression relationships. We aim for a DNN for image compression purpose that has better generalization property and reduced training time and support real time operation. The use of ReLUs which map more plausibly to biological neurons, makes the training of our DNN significantly faster, shortens the encoding/decoding time, and improves its generalization ability. The introduction of the ReLUs establishes an efficient gradient propagation, induces sparsity in the proposed network, and is efficient in terms of computations making these networks suitable for real time compression systems. Experiments performed on standard real world images show that using ReLUs instead of logistic sigmoid units speeds up the training of the DNN by converging markedly faster. The evaluation of objective and subjective quality of reconstructed images also proves that our DNN achieves better generalization as most of the images are never seen by the network before.