Distortion-optimal transmission of progressive images over channels with random bit errors and packet erasures

We present a statistical optimization framework for solving the end-to-end problem of progressive transmission of images over noisy channels. We consider the impacts of transmission bit errors as well as packet erasures. To cope with the impact of random bit errors, we formulate an optimization problem aimed at minimizing the end-to-end expected distortion of a reconstructed image subject to rate and efficiency constraints. In order to eliminate the impact of packet erasures, we propose utilizing an algorithm that is capable of statistically guaranteeing the delivery of a packet set associated with the progressive bitstream of an image source. Using receiver feedback, our framework is capable of effectively coping with the channel loss effects characterized by the Gilbert-Elliott model.

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