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 …