Adaptive binary vector quantization using hamming codes

Hamming codes are studied as a means of adaptive vector quantization of binary images. The idea is to minimize, within the equivalence class of a Hamming code, the expected quantization distortion, while bounding the maximum distortion per vector to prevent burst quantization errors in a binary image. Some interesting and useful relationships between distinct Hamming codes are presented. These findings can lead to efficient algorithms for designing adaptive binary vector quantizers whose codebooks can adapt to sources of smoothly changing statistics.