This paper proposes a distributed compressive sensing (CS) scheme for robust image transmission over unknown or time-varying channels with highly correlated images at the decoder. A compressed thumbnail is first transmitted after digital forward error correction (FEC) and modulation to retrieve highly correlated images and generate a side information (SI) at the decoder. The current residual image after subtracting the decompressed thumbnail is then coded and transmitted by CS through a very dense constellation without FEC. The linear representation of the residual signal by CS measurements and rateless sampling makes it able to achieve graceful degradation and bandwidth scalability without channel feedback. Moreover, a transform-domain power allocation is employed before random sampling to protect against channel errors. At the decoder, both the nonlocal correlations within the original image and the correlation with the SI are exploited in CS decoding via a low-rank regulation on similar patches. After CS decoding, a block-wise minimum-mean-square-error (MMSE) reconstruction using the SI is further performed in the spatial domain to enhance the reconstruction quality. Simulations on landmark images and an unknown Gaussian channel show that an up to 10 dB gain is achieved at low channel SNRs compared with the state-of-the-art uncoded image transmission scheme, i.e. SoftCast, when highly correlated images are available at the decoder.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper proposes a distributed compressive sensing (CS) scheme for robust image transmission over unknown or time-varying channels with highly correlated images at the decoder. A compressed thumbnail is first transmitted after digital forward error correction (FEC) and modulation to retrieve highly correlated images and generate a side information (SI) at the decoder. The current 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