Multimode image coding for noisy channels

We attack the problem of designing robust image compression schemes for transmission over noisy channels. To achieve the dual goals of high compression efficiency and low sensitivity to channel noise, we introduce a multimode coding framework. Multimode coders are quasi-fixed length in nature, and this enables optimal tradeoff between the compression capability of variable-length coding and the robustness of fixed length coding. The framework lends itself to a natural incorporation of conventional source-channel coding mechanisms, and thus provides a joint design methodology for the source and channel coding components. We first derive a general Lagrangian based algorithm to design the multimode coder which optimizes the overall rate-distortion performance. We then apply our framework to develop multimode image coding (MIC) schemes for noisy channels. We demonstrate the benefits of the multimode approach by developing two specific image compression algorithms in conjunction with the following source-channel coding mechanisms: (i) channel optimized quantizers, and (ii) transmission energy allocation. Simulations demonstrate that substantial gains of up to 7dB can be achieved by MICs over conventional approaches.