Fast list Viterbi decoding and application for source-channel coding of images

A list Viterbi algorithm (LVA) finds n most likely paths in a trellis diagram of a convolutional code. One of the most efficient LVAs is the tree-trellis algorithm of Soong and Huang. We propose a new implementation of this algorithm. Instead of storing the candidate paths in a single list sorted according to the metrics of the paths, we show that it is computationally more efficient to use several unsorted lists, where all paths of the same list have the same metric. For an arbitrary integer bit metric, both the time and space complexity of our implementation are linear in n. Experimental results for a binary symmetric channel and an additive white Gaussian noise channel show that our implementation is much faster than all previous LVAs. This allows us to consider a large number of paths in acceptable time. As an application, we show that by increasing the number of candidate paths, one can significantly improve the performance of a popular progressive source-channel coding system that protects an embedded image code with a concatenation of an outer error detecting code and an inner error correcting convolutional code

Fast list Viterbi decoding and application for source-channel coding of images

A list Viterbi algorithm (LVA) finds the n best paths in a trellis. We propose a new implementation of the tree-trellis LVA. Instead of storing all paths in a single sorted list, we show that it is more efficient to use several lists, where all paths of the same list have the same metric. For an integer metric, both the time and space complexity of our implementation are linear in n. Experimental results show that our implementation is much faster than all pre-vious LVAs. This allows us to consider a large number of paths in acceptable time, which significantly improves the performance of a popular progressive source-channel coding system that protects embedded data with a concatenation of an outer error detecting code and an inner error correcting convolutional code