In this paper, we aimto propose an image compression and reconstruction strategy under the compressed sensing (CS) framework\nto enable the green computation and communication for the Internet of Multimedia Things (IoMT). The core idea is to explore\nthe statistics of image representations in the wavelet domain to aid the reconstruction method design. Specifically, the energy\ndistribution of natural images in the wavelet domain is well characterized by an exponential decay model and then used in the\ntwo-step separate image reconstruction method, by which the row-wise (or column-wise) intermediates and column-wise (or rowwise)\nfinal results are reconstructed sequentially. Both the intermediates and the final results are constrained to conform with the\nstatistical prior by using a weight matrix. Two recovery strategies with different levels of complexity, namely, the direct recovery\nwith fixed weight matrix (DR-FM) and the iterative recovery with refined weight matrix (IR-RM), are designed to obtain different\nquality of recovery. Extensive simulations show that bothDR-FM and IR-RM can achievemuch better image reconstruction quality\nwith much faster recovery speed than traditional methods.
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