Optimal binary vector quantization via enumeration of covering codes
Binary vector quantization (BVQ) refers to block coding of binary vectors under a fidelity measure, Covering codes were studied as a means of lattice BVQ. But a further source coding problem hidden in the equivalence of covering codes has seemingly eluded attention, Given a d-dimensional hypercube (code space), equivalent covering codes of the same covering radius but of different codewords have different expected BVQ distortions for a general probability mass function, Thus one can minimize, within the code equivalence, the expected distortion over all different covering codes, This leads a two-stage optimization scheme for BVQ design. First we use an optimal covering code to minimize the maximum per-vector distortion at a given rate, Then under the minmax constraint, we minimize the expected quantization distortion, This minmax constrained BVQ method (MCBVQ) controls both the maximum and average distortions, and hence improves subjective quality of compressed binary images, MCBVQ also avoids poor local minima that may trap the generalized Lloyd method, The [7, 4] Hamming code and [8, 4] extended Hamming code are found to be particularly suitable for MCBVQ on binary images, An efficient and simple algorithm is introduced to enumerate all distinct [7, 4] Hamming/[8, 4] extended Hamming codes and compute the corresponding expected distortions in optimal MCBVQ design, Furthermore, MCBVQ using linear covering codes has a compact codebook and a fast syndrome-encoding algorithm.
binary vector quantization covering codes hamming codes binary image compression distortion measures