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Compressive sensing is a new technique in signal processing which can recover a sparse signal vector via a much smaller of non-adaptive, linear measurements than the dimension of the signal vector. In this paper, we applied compressive sensing to a joint source compression-channel coding scheme. With analysis of the reconstruction error of the sparse representation of natural image signals, we argue...
During video streaming over packet-erasure network, packets may lose due to network congestion. Since video packets have different significance and validity for decoding, the streaming system is expected to transmit the most critical packets when congestion occurs. In this paper, a packet criticality model is proposed and then a video transmission framework based on this model is presented. The criticality...
A novel compression framework called distributed compressed hyper spectral image sensing (DCHIS) is proposed in this paper. In our framework, the random measurements of each spectral band are obtained using compressed sensing (CS) encoding independently at the encoder. At the decoder, a new reconstruction algorithm with the proposed initialization and stopping criterion is applied to reconstruct the...
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