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Recently, vector quantization has become noted as a highly efficient coding method of image and voice data. So far, many of the highly efficient coding problems, or service coding problems, have been studied separately from channel coding problems. This paper reconsiders vector quantization jointly optimizing source coding and channel coding, and proposes a new vector quantizer for noisy channels. Vector quantizers for binary symmetric channels are designed for memoryless Gaussian source, Gauss‐Markov source and the real images, and are compared with the conventional vector quantizer which does not take account of channel errors. As a result, it is shown that the performance of the proposed vector quantizer is improved without adding the redundancy for error correction, and the improvement of the performance of the proposed vector quantizer for noisy channels over the conventional vector quantizer becomes significant for highly correlated sources and longer block length.

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