Joint Source and Channel Coding for Image Transmission over Time Varying Channels

In this paper, the joint source and channel coding for progressive image transmission over channels with varying SNR is considered. Since the feedback of channel status information generally lags behind the channel variation and the optimization process often causes high computational complexity, it is appropriate to optimally allocate the given bandwidth between the source and channel codes based on the statistics of the channel rather than a specific SNR. In such case, the optimization objective function is no longer in a recursive format and the computational complexity with exhaustive search for the optimal allocation is prohibitive. In this paper, we present a simple yet very effective genetic algorithm (GA) based optimization method so that the near-optimal channel rate allocation can be obtained through crossover and mutation operations over a candidate pool. Simulation shows that this GA-based method always approaches to the optimal results of the brute force search for the considered scenario, but with much lower complexity.