Subband image coding using entropy-coded quantization over noisy channels

The design and performance of two entropy-coded subband image-coding schemes are studied. The difference between these schemes is the procedure used for encoding the lowest-frequency subband; predictive coding is used in one system and transform coding in the other. Other subbands are encoded using zero-memory quantization. It is shown that, in the absence of channel noise, both schemes perform better than other subband coding methods. The unacceptable sensitivity of these schemes to transmission noise is demonstrated, and a combined source/channel coding scheme in which rate-compatible punctured convolutional codes are used to provide protection against channel noise is developed. In the presence of channel noise, these channel-optimized schemes offer dramatic performance improvements over schemes designed based on a noiseless-channel assumption.