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.
Highlights
We present an energy-efficient compressive encoder, in which Compressive Sensing (CS) measurements are allocated based on the sparse degree of each image block
Various experiments are conducted to evaluate the performance of the proposed compressive image coding
We evaluate the encoding complexity, and the execution time of our encoder is compared with H.264/AVC [22], HEVC [23], and DISCOVER [24], which are the traditional video coding systems
Summary
There are many applications in the IoT framework, e.g., environmental monitoring, surveillance, device tracing, et al One benefit from the framework of IoT is that a large amount of data can be gathered in a central processing server, so that we can analyze the data and achieve the valuable information in real time. Since visual sensors are a major energy consumer on IoT, many existing works have made great efforts to design energy-efficient ones, e.g., CITRIC [4], MicrelEye [5]. These visual sensors are only the size of a coin, and their battery can last as long as a dozen hours. Image coding is still a heavy burden for visual sensors, e.g., the processing part, which often uses JPEG [10]
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