Joint optimization of lattice vector quantizer and entropy coder for a Laplacian source
Wonha Kim, Yu Hen Hu, Truong Q. Nguyen · 2002
This paper presents a joint optimization algorithm for lattice vector quantization (LVQ) and entropy coding for a Laplacian source at all ranges of bit rates. Entropy-constrained lattice vector quantizers (ECLVQs) are often used in practical coding systems. In order to develop an ECLVQ design algorithm, we derive estimation expressions for both distortion and entropy. From these estimations, we develop an algorithm that jointly optimizes LVQ and the entropy coder pair for a given entropy rate. Compared to previously reported approaches, the approach reported quickly computes a highly accurate optimal ECLVQ at all ranges of bit rates. Since a Laplacian source represents a wide class of subband transformed data, the algorithm can be readily applied as a subband coding method. When the proposed algorithm is applied to a wavelet based image coding, the coding performance surpasses those of any previously reported subband coders, especially at low bit rates.