The far end error decoder with application to image transmission

We present two new decoding algorithms that estimate the output (path in the trellis) of a Markov source observed through a discrete memoryless channel with special application to convolutional codes. In a change of paradigm, these decoders do not exclusively aim at a low average error probability, but rather try to maximize the contiguously correct decoded subpath (where a subpath begins with the first symbol) - a concept termed far end error decoding. In addition to path estimation, these decoders provide a reliability about each subpath and, thus, enable us to localize the first decoding error without spending additional redundancy. This approach is motivated by the fact that in many applications it is much more important to deliver only error free data to the source decoder rather than to achieve a low error probability. The significant performance gain possible with this new approach is demonstrated for the example of SPIHT coded images