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It is well known that convolutional codes can be optimally decoded by using the Viterbi Algorithm (VA). We propose a decoding technique where the VA is applied to identify the error vector rather than the information message. We previously focused on convolutional coders of rate ½ [4] [5]. Here we generalize the method to codes of any rate. We show that, with the proposed type of decoding, the exhaustive computation of a vast majority of state to state iterations is unnecessary. Hence, performance close to optimum is achievable with a significant reduction of complexity. The higher the SNR, the greater the improvement for reduction in complexity. For instance, for SNR greater than 3 dB, a five fold reduction in complexity for the computation of ACS (Add Compare Select) is achieved.

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Joint source-channel decoding of Variable Length C o d a (VLC) for image and video streaming transmission over unreliable links, such as wireless networks, is a subject of increasing interest. Paper 1141 shows that much more residual source redundancy can be exploited when both types of correlation: (i) the correlation between hits inside a VLC codeword as well as (ii) the correlation hetween VLC codewords of a VLC sequence unit (e.g. corresponding to one image hlock), are taken into account. Paper 1151 proposes an optimum Maximum Likelihood (ML) decoder of VLC sequences which can exploit these two kinds of correlation and outperform the existing VLC decoders. This paper proposes a reduced complexity version of this optimal VLC decoder. Simulation results show that the complexity is reduced hy 5 in terms of numher of possible candidates, while the performance is only slizhtly sub-optimal compared with the optimal venion of paper 051.Index Terms-VLC decoding. comoressed image and video -.-~ decoding, source redundancy, joint source-channel decoding.