{'fraction_non_alphanumeric': 0.01261467889908257, 'fraction_numerical': 0.0, 'mean_word_length': 5.984, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Symbol-based iterative decoding of convolutionally encoded multiple descriptions

Transmission of convolutionally encoded multiple descriptions over noisy channels can benefit from the use of iterative source-channel decoding. The authors first modified the BCJR algorithm in a way that symbol a posteriori probabilities can be derived and used as extrinsic information to improve the iterative decoding between the source and channel decoders. The authors also formulate a recursive implementation for the source decoder that processes reliability information received on different channels and combines them with inter-description correlation to estimate the transmitted quantiser index. Simulation results are presented for two-channel scalar quantisation of Gauss - Markov sources which demonstrate the error-resilience capabilities of symbol-based iterative decoding.