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

• Main Authors: Ran Li, Xiaomeng Duan, Xu Li, Wei He, Yanling Li
• Format: Article
• Language: English
• Published: MDPI AG 2018-04-01
• Series: Sensors
• Subjects: Green IoT; compressive sensing; image coding; gradient field; linear projection
• Online Access: http://www.mdpi.com/1424-8220/18/4/1231

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.