Optimization for image transmission over varying channel with MCMC
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RESEARCH
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Optimization for image transmission over varying channel with MCMC Xiaobin Wu, Lei Cao* and Paul Goggans
Abstract Existing work in media transmission generally assumes that the channel condition is stationary. However, communication channels are often varying with time in practice. Adaptive design needs frequent feedback for channel updates, which is often impractical due to the complexity and delay. In this article, we design the unequal error protection for image transmission over noisy varying channels based on their distribution functions. Since the channel effect must be marginalized in order to find the appropriate rate allocation, the optimization problem is very complex. We propose to solve this problem using the Markov Chain Monte Carlo (MCMC) method. The cost function is first mapped into a multi-variable probability distribution. Then, with the “detailed balance”, MCMC is designed to generate samples from the mapped stationary probability distribution so that the optimal solution is the one that gives the lowest data distortion. We also show that the rate allocation design considering the channel probability function works better than the design considering the mean value of the channel. Keywords: Progressive image transmission, Optimal rate-allocation, Markov chain monte carlo
Introduction Progressive image compression, such as SPIHT [1], is an approach that exploits the inherent similarities across the sub-bands in a wavelet decomposition of an image, and the algorithm codes the most important wavelet coefficients first, and transmits the bits so that an increasingly refined copy of the original image can be obtained progressively. The progressive compression is widely used in many applications, because the media can be restored with the best quality by receiving a sequence of continuous error-free data. However in the coded data stream, any error bit due to channel noise would cause the loss of synchronization between the sender and receiver, which means that all the data after that bit error has to be completely discarded. Therefore, an important issue in image transmission is to design a protection strategy for the source data, i.e., allocating channel code rates to different data packets, based on the channel condition and the ratedistortion feature of the source, in order to optimize the overall recovery quality of image in the noise channel. In [2], the cyclic redundancy check codes and rate compatible punctured codes (CRC/RCPC) were employed to
protect the SPIHT coded data and obtained performance better than previous results in binary symmetric channels (BSCs). This work used equal error protection and was then extended to the product code protection [3] when the Gilbert–Elliot channel (GEC) model was considered. Since then, many error-control solutions for progressive image transmission [3-11] have been proposed. In these methods, different codes are used, including CRC/RCPC [5,7,8], CRC/RCPT codes [6,9-11], Reed-Solomon (RS) codes [4,11] and their product codes. Different chan
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