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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 \u201cdetailed balance\u201d, 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.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1687-1499-2012-275","type":"journal-article","created":{"date-parts":[[2012,8,30]],"date-time":"2012-08-30T15:23:11Z","timestamp":1346340191000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimization for image transmission over varying channel with MCMC"],"prefix":"10.1186","volume":"2012","author":[{"given":"Xiaobin","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Goggans","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2012,8,30]]},"reference":[{"key":"526_CR1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1109\/76.499834","volume":"6","author":"Said A","year":"1996","unstructured":"A Said, WA Pearlman: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. 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