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Sensors 2018, 18(4), 1231; doi:10.3390/s18041231

An Energy-Efficient Compressive Image Coding for Green Internet of Things (IoT)

School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
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Received: 20 March 2018 / Revised: 14 April 2018 / Accepted: 14 April 2018 / Published: 17 April 2018
(This article belongs to the Special Issue Green Communications and Networking for IoT)
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Abstract

Aimed at a low-energy consumption of Green Internet of Things (IoT), this paper presents an energy-efficient compressive image coding scheme, which provides compressive encoder and real-time decoder according to Compressive Sensing (CS) theory. The compressive encoder adaptively measures each image block based on the block-based gradient field, which models the distribution of block sparse degree, and the real-time decoder linearly reconstructs each image block through a projection matrix, which is learned by Minimum Mean Square Error (MMSE) criterion. Both the encoder and decoder have a low computational complexity, so that they only consume a small amount of energy. Experimental results show that the proposed scheme not only has a low encoding and decoding complexity when compared with traditional methods, but it also provides good objective and subjective reconstruction qualities. In particular, it presents better time-distortion performance than JPEG. Therefore, the proposed compressive image coding is a potential energy-efficient scheme for Green IoT. View Full-Text
Keywords: Green IoT; compressive sensing; image coding; gradient field; linear projection Green IoT; compressive sensing; image coding; gradient field; linear projection
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Li, R.; Duan, X.; Li, X.; He, W.; Li, Y. An Energy-Efficient Compressive Image Coding for Green Internet of Things (IoT). Sensors 2018, 18, 1231.

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