Abstract
Due to complexity and delay constraints a usually significant amount of residual redundancy remains in the source samples after source coding. This residual redundancy can be exploited by iterative source-channel decoding for error concealment and quality improvements. One key design issue in joint source-channel (de-)coding is the index assignment. Besides conventional index assignments optimized index assignments have been developed, e.g., considering zeroth or first order a priori information of the source samples. However, in real-world scenarios it is unlikely that the amount of residual redundancy is constant over time and thus it may occur that the just deployed index assignment is suboptimal at times when the residual redundancy differs too much from the amount that it is optimized for. In this paper the performance of optimized index assignments is examined that consider first order a priori knowledge under such suboptimal conditions.
Highlights
Since the discovery of Turbo codes [1], which allow for channel coding close to the Shannon limit with moderate complexity, the Turbo principle of exchanging extrinsic information has been extended to various components of the receiver chain
Unlike Turbo channel decoding, which aims at minimizing the bit error rate, Iterative source-channel decoding (ISCD) mainly aims at error concealment and signal restoration which is not necessarily connected to a lower bit error rate, but to a higher parameter signal-to-noise ratio (SNR)
Delay or complexity constraints prevent a complete removal of the residual redundancy and in practice, a quite large amount of residual redundancy remains in the source codec parameters, which can be exploited by ISCD
Summary
Since the discovery of Turbo codes [1], which allow for channel coding close to the Shannon limit with moderate complexity, the Turbo principle of exchanging extrinsic information has been extended to various components of the receiver chain. ISCD exploits the a priori knowledge on the residual redundancy of the source codec parameters that remains after imperfect source coding. In [6, 7] index assignments have been introduced that are optimized considering a nonuniform probability distribution, i.e., the zeroth order a priori information, of source samples. Further enhanced index assignments have been presented in [8] and the corresponding optimization process even takes the first order a priori information, e.g., the autocorrelation, of the source samples into account.
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