Title :
Error exponents for successive refinement by partitioning
Author :
Kanlis, Angelos ; Narayan, Prakash
Author_Institution :
Dept. of Electr. Eng., Maryland Univ., College Park, MD, USA
fDate :
1/1/1996 12:00:00 AM
Abstract :
Given a discrete memoryless source (DMS) with probability mass function P, we seek first an asymptotically optimal description of the source with distortion not exceeding Δ1, followed by an asymptotically optimal refined description with distortion not exceeding Δ2<Δ1. The rate-distortion function for successive refinement by partitioning, denoted R(P, Δ1 , Δ2), is the overall optimal rate of these descriptions obtained via a two-step coding process. We determine the error exponents for this two-step coding process, namely, the negative normalized asymptotic log likelihoods of the event that the distortion in either step exceeds its prespecified acceptable value, and of the conditional event that the distortion in the second step exceeds the prespecified value given the rate and distortion of the code for the first step. We show that even when the rate-distortion functions for one- and two-step coding coincide, the error exponent in the former case may exceed those in the latter
Keywords :
coding errors; encoding; error statistics; probability; rate distortion theory; asymptotically optimal description; asymptotically optimal refined description; conditional event; discrete memoryless source; distortion; error exponents; negative normalized asymptotic log likelihoods; optimal rate; partitioning; probability mass function; rate distortion function; successive refinement; two step coding process; Artificial intelligence; Block codes; Communication systems; Decoding; Distortion measurement; Information theory; Random variables; Rate distortion theory; Rate-distortion;
Journal_Title :
Information Theory, IEEE Transactions on