Source-Optimized Channel Coding for Digital Transmission Channels

We present a new class of nonlinear block codes called source-optimized channel codes (SOCCs), which are particularly designed for parametric source encoding of speech, audio, and video. In contrast to conventional channel codes, the new codes are not optimized for minimizing residual bit-error rate, but maximizing the signal-to-noise ratio of transmitted source codec parameters. The decoding of SOCCs is not based on bit-error correction, but on parameter estimation. We compare SOCCs with other approaches to joint source/channel coding such as channel-optimized vector quantization, channel-constrained vector quantization, unequal error protection, and source-controlled channel decoding. In terms of performance, SOCCs show better robustness if under channel mismatch conditions. For real-world applications, SOCCs are attractive, since the separation of source and channel codec is preserved.