Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to ...Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.展开更多
基金The National Natural Science Foundation of China(1117126911401074)+8 种基金the Ph.D.Program Foundation of Ministry of Education of China(20110201110027)the China Postdoctoral Science Foundation(2013M531311)the Research Fund of Educational Commission of Henan Province of China(14B11002014B11002114B110025)the Henan Scienti?c and Technological Research Project(132102310309)the Doctoral Foundation of Henan University of Science and Technology(09001625)the Science Foundation for Cultivating Innovation Ability of Henan University of Science and Technology(2014ZCX009)the Youth Scienti?c Foundation of Henan University of Science and Technology(2012QN029)
基金supported in part by National Natural Science Foundation of China (61101114, 61671324) the Program for New Century Excellent Talents in University (NCET-12-0401)
文摘Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.
