Multimode image coding for noisy channels

We attack the problem of robust and efficient image compression 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 allow optimization of the tradeoff between the compression capability of variable-length coding and the robustness to channel errors of fixed length coding. We apply our framework to develop multimode image coding (MIC) schemes for noisy channels, based on the adaptive DCT. The robustness of the proposed MIC is further enhanced by the incorporation of a channel protection scheme suitable for the constraints on complexity and delay. To demonstrate the power of the technique we develop two specific image coding algorithms optimized for the binary symmetric channel. The first, MIC1, incorporates channel optimized quantizers and the second, MIC2, uses rate compatible punctured convolutional codes within the multimode framework. Simulations demonstrate that the multimode coders obtain significant performance gains of up to 6 dB over conventional fixed length coding techniques.