Li Ran, Duan Xiaomeng, Li Xu, He Wei, Li Yanling
School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China.
Sensors (Basel). 2018 Apr 17;18(4):1231. doi: 10.3390/s18041231.
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.
针对绿色物联网的低能耗需求，本文提出了一种节能型压缩图像编码方案，该方案基于压缩感知（CS）理论提供了压缩编码器和实时解码器。压缩编码器基于块梯度场对每个图像块进行自适应测量，该梯度场对块稀疏度分布进行建模，实时解码器通过最小均方误差（MMSE）准则学习得到的投影矩阵对每个图像块进行线性重构。编码器和解码器都具有较低的计算复杂度，因此只消耗少量能量。实验结果表明，与传统方法相比，该方案不仅具有较低的编码和解码复杂度，而且还提供了良好的客观和主观重构质量。特别是，它比JPEG具有更好的时间-失真性能。因此，所提出的压缩图像编码是一种潜在的绿色物联网节能方案。
