ASYMPTOTIC ANALYSIS OF MULTIPLE DESCRIPTION LATTICE VECTOR QUANTIZATION

Recent results have shown that general K-channel multiple-description-coding (MDC) approaches often have significant advantages over conventional two-channel MDC methods. We provide new asymptotic re-sults to describe the performance of a general K-channel symmetric MDC lattice vector quantizer (MDLVQ). We consider a memoryless L-dimensional source with probability density function f and differential entropy h(f) <∞. We control the redundancy with a parameter a ∈ (0, 1) and consider a symmetric MDC with a K-tuple of {R,R, · · · , R} as side quantizer rates. We show that if κ out of K descriptions are received, then the central distortion D(K,K) and the side distortions D(K,κ) satisfy lim R→∞ D(K,K)22R[1+a(K−1)] = G(Λ)22h(f), li